Henrik I. Christensen

David Paz, Srinidhi Kalgundi Srinivas, Yunchao Yao et al.

This work introduces a new approach for joint detection of centerlines based on image data by localizing the features jointly in 2D and 3D. In contrast to existing work that focuses on detection of visual cues, we explore feature extraction methods that are directly amenable to the urban driving task. To develop and evaluate our approach, a large urban driving dataset dubbed AV Breadcrumbs is automatically labeled by leveraging vector map representations and projective geometry to annotate over 900,000 images. Our results demonstrate potential for dynamic scene modeling across various urban driving scenarios. Our model achieves an F1 score of 0.684 and an average normalized depth error of 2.083. The code and data annotations are publicly available.

Anwesan Pal, Sahil Wadhwa, Ayush Jaiswal et al.

Multi-turn textual feedback-based fashion image retrieval focuses on a real-world setting, where users can iteratively provide information to refine retrieval results until they find an item that fits all their requirements. In this work, we present a novel memory-based method, called FashionNTM, for such a multi-turn system. Our framework incorporates a new Cascaded Memory Neural Turing Machine (CM-NTM) approach for implicit state management, thereby learning to integrate information across all past turns to retrieve new images, for a given turn. Unlike vanilla Neural Turing Machine (NTM), our CM-NTM operates on multiple inputs, which interact with their respective memories via individual read and write heads, to learn complex relationships. Extensive evaluation results show that our proposed method outperforms the previous state-of-the-art algorithm by 50.5%, on Multi-turn FashionIQ -- the only existing multi-turn fashion dataset currently, in addition to having a relative improvement of 12.6% on Multi-turn Shoes -- an extension of the single-turn Shoes dataset that we created in this work. Further analysis of the model in a real-world interactive setting demonstrates two important capabilities of our model -- memory retention across turns, and agnosticity to turn order for non-contradictory feedback. Finally, user study results show that images retrieved by FashionNTM were favored by 83.1% over other multi-turn models. Project page: https://sites.google.com/eng.ucsd.edu/fashionntm

Hengyuan Zhang, David Paz, Yuliang Guo et al.

Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when generating and maintaining HD maps at scale. While recent online mapping methods have started to emerge, their performance especially for longer ranges is limited by heavy occlusion in dynamic environments. With these considerations in mind, our work focuses on leveraging lightweight and scalable priors-Standard Definition (SD) maps-in the development of online vectorized HD map representations. We first examine the integration of prototypical rasterized SD map representations into various online mapping architectures. Furthermore, to identify lightweight strategies, we extend the OpenLane-V2 dataset with OpenStreetMaps and evaluate the benefits of graphical SD map representations. A key finding from designing SD map integration components is that SD map encoders are model agnostic and can be quickly adapted to new architectures that utilize bird's eye view (BEV) encoders. Our results show that making use of SD maps as priors for the online mapping task can significantly speed up convergence and boost the performance of the online centerline perception task by 30% (mAP). Furthermore, we show that the introduction of the SD maps leads to a reduction of the number of parameters in the perception and reasoning task by leveraging SD map graphs while improving the overall performance. Project Page: https://henryzhangzhy.github.io/sdhdmap/.

Jing-Yan Liao, Parth Doshi, Zihan Zhang et al.

While High Definition (HD) Maps have long been favored for their precise depictions of static road elements, their accessibility constraints and susceptibility to rapid environmental changes impede the widespread deployment of autonomous driving, especially in the motion forecasting task. In this context, we propose to leverage OpenStreetMap (OSM) as a promising alternative to HD Maps for long-term motion forecasting. The contributions of this work are threefold: firstly, we extend the application of OSM to long-horizon forecasting, doubling the forecasting horizon compared to previous studies. Secondly, through an expanded receptive field and the integration of intersection priors, our OSM-based approach exhibits competitive performance, narrowing the gap with HD Map-based models. Lastly, we conduct an exhaustive context-aware analysis, providing deeper insights in motion forecasting across diverse scenarios as well as conducting class-aware comparisons. This research not only advances long-term motion forecasting with coarse map representations but additionally offers a potential scalable solution within the domain of autonomous driving.

Yiding Qiu, Henrik I. Christensen

3D scene graph prediction is a task that aims to concurrently predict object classes and their relationships within a 3D environment. As these environments are primarily designed by and for humans, incorporating commonsense knowledge regarding objects and their relationships can significantly constrain and enhance the prediction of the scene graph. In this paper, we investigate the application of commonsense knowledge graphs for 3D scene graph prediction on point clouds of indoor scenes. Through experiments conducted on a real-world indoor dataset, we demonstrate that integrating external commonsense knowledge via the message-passing method leads to a 15.0 % improvement in scene graph prediction accuracy with external knowledge and $7.96\%$ with internal knowledge when compared to state-of-the-art algorithms. We also tested in the real world with 10 frames per second for scene graph generation to show the usage of the model in a more realistic robotics setting.

Hengyuan Zhang, Jing-Yan Liao, David Paz et al.

Many outdoor autonomous mobile platforms require more human identity anonymized data to power their data-driven algorithms. The human identity anonymization should be robust so that less manual intervention is needed, which remains a challenge for current face detection and anonymization systems. In this paper, we propose to use the skeleton generated from the state-of-the-art human pose estimation model to help localize human heads. We develop criteria to evaluate the performance and compare it with the face detection approach. We demonstrate that the proposed algorithm can reduce missed faces and thus better protect the identity information for the pedestrians. We also develop a confidence-based fusion method to further improve the performance.

David Paz, Narayanan E. Ranganatha, Srinidhi K. Srinivas et al.

This research work seeks to explore and identify strategies that can determine road topology information in 2D and 3D under highly dynamic urban driving scenarios. To facilitate this exploration, we introduce a substantial dataset comprising nearly one million automatically labeled data frames. A key contribution of our research lies in developing an automatic label-generation process and an occlusion handling strategy. This strategy is designed to model a wide range of occlusion scenarios, from mild disruptions to severe blockages. Furthermore, we present a comprehensive ablation study wherein multiple centerline detection methods are developed and evaluated. This analysis not only benchmarks the performance of various approaches but also provides valuable insights into the interpretability of these methods. Finally, we demonstrate the practicality of our methods and assess their adaptability across different sensor configurations, highlighting their versatility and relevance in real-world scenarios. Our dataset and experimental models are publicly available.

Ruffin White, Gianluca Caiazza, Henrik I. Christensen et al.

Security of robotics systems, as well as of the related middleware infrastructures, is a critical issue for industrial and domestic IoT, and it needs to be continuously assessed throughout the whole development lifecycle. The next generation open source robotic software stack, ROS2, is now targeting support for Secure DDS, providing the community with valuable tools for secure real world robotic deployments. In this work, we introduce a framework for procedural provisioning access control policies for robotic software, as well as for verifying the compliance of generated transport artifacts and decision point implementations.

Narayanan Elavathur Ranganatha, Hengyuan Zhang, Shashank Venkatramani et al.

Vector maps are essential in autonomous driving for tasks like localization and planning, yet their creation and maintenance are notably costly. While recent advances in online vector map generation for autonomous vehicles are promising, current models lack adaptability to different sensor configurations. They tend to overfit to specific sensor poses, leading to decreased performance and higher retraining costs. This limitation hampers their practical use in real-world applications. In response to this challenge, we propose a modular pipeline for vector map generation with improved generalization to sensor configurations. The pipeline leverages probabilistic semantic mapping to generate a bird's-eye-view (BEV) semantic map as an intermediate representation. This intermediate representation is then converted to a vector map using the MapTRv2 decoder. By adopting a BEV semantic map robust to different sensor configurations, our proposed approach significantly improves the generalization performance. We evaluate the model on datasets with sensor configurations not used during training. Our evaluation sets includes larger public datasets, and smaller scale private data collected on our platform. Our model generalizes significantly better than the state-of-the-art methods.

Bo Ai, Liu Dai, Nico Bohlinger et al. · stanford

Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodiment scaling laws, the hypothesis that increasing the number of training embodiments improves generalization to unseen ones, using robot locomotion as a test bed. We procedurally generate ~1,000 embodiments with topological, geometric, and joint-level kinematic variations, and train policies on random subsets. We observe positive scaling trends supporting the hypothesis, and find that embodiment scaling enables substantially broader generalization than data scaling on fixed embodiments. Our best policy, trained on the full dataset, transfers zero-shot to novel embodiments in simulation and the real world, including the Unitree Go2 and H1. These results represent a step toward general embodied intelligence, with relevance to adaptive control for configurable robots, morphology co-design, and beyond.

Hongzhan Yu, Seth Farrell, Ryo Yoshimitsu et al.

Learning-based methods for constructing control barrier functions (CBFs) are gaining popularity for ensuring safe robot control. A major limitation of existing methods is their reliance on extensive sampling over the state space or online system interaction in simulation. In this work we propose a novel framework for learning neural CBFs through a fixed, sparsely-labeled dataset collected prior to training. Our approach introduces new annotation techniques based on out-of-distribution analysis, enabling efficient knowledge propagation from the limited labeled data to the unlabeled data. We also eliminate the dependency on a high-performance expert controller, and allow multiple sub-optimal policies or even manual control during data collection. We evaluate the proposed method on real-world platforms. With limited amount of offline data, it achieves state-of-the-art performance for dynamic obstacle avoidance, demonstrating statistically safer and less conservative maneuvers compared to existing methods.

Christopher D'Ambrosia, Florian Richter, Zih-Yun Chiu et al.

Controlling robotic manipulators via visual feedback requires a known coordinate frame transformation between the robot and the camera. Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation. These errors result in poor localization of robotic manipulators and create a significant challenge for applications that rely on precise interactions between manipulators and the environment. In this work, we estimate the camera-to-base transform and joint angle measurement errors for surgical robotic tools using an image based insertion-shaft detection algorithm and probabilistic models. We apply our proposed approach in both a structured environment as well as an unstructured environment and measure to demonstrate the efficacy of our methods.

Junjie Ye, David Paz, Hengyuan Zhang et al.

Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent approaches have shown success in perceiving driving topology using vehicle-mounted sensors, their scalability is hindered by the reliance on training data captured by consistent sensor configurations. We identify that the key factor in scalable lane perception and topology reasoning is the elimination of this sensor-dependent feature. To address this, we propose SMART, a scalable solution that leverages easily available standard-definition (SD) and satellite maps to learn a map prior model, supervised by large-scale geo-referenced high-definition (HD) maps independent of sensor settings. Attributed to scaled training, SMART alone achieves superior offline lane topology understanding using only SD and satellite inputs. Extensive experiments further demonstrate that SMART can be seamlessly integrated into any online topology reasoning methods, yielding significant improvements of up to 28% on the OpenLane-V2 benchmark.

Shresth Grover, Akshay Gopalkrishnan, Bo Ai et al.

Vision-language-action (VLA) models finetuned from vision-language models (VLMs) hold the promise of leveraging rich pretrained representations to build generalist robots across diverse tasks and environments. However, direct fine-tuning on robot data often disrupts these representations and limits generalization. We present a framework that better preserves pretrained features while adapting them for robot manipulation. Our approach introduces three components: (i) a dual-encoder design with one frozen vision encoder to retain pretrained features and another trainable for task adaptation, (ii) a string-based action tokenizer that casts continuous actions into character sequences aligned with the model's pretraining domain, and (iii) a co-training strategy that combines robot demonstrations with vision-language datasets emphasizing spatial reasoning and affordances. Evaluations in simulation and on real robots show that our method improves robustness to visual perturbations, generalization to novel instructions and environments, and overall task success compared to baselines.

Abdulaziz Almuzairee, Rohan Patil, Dwait Bhatt et al.

Vision is well-known for its use in manipulation, especially using visual servoing. Due to the 3D nature of the world, using multiple camera views and merging them creates better representations for Q-learning and in turn, trains more sample efficient policies. Nevertheless, these multi-view policies are sensitive to failing cameras and can be burdensome to deploy. To mitigate these issues, we introduce a Merge And Disentanglement (MAD) algorithm that efficiently merges views to increase sample efficiency while simultaneously disentangling views by augmenting multi-view feature inputs with single-view features. This produces robust policies and allows lightweight deployment. We demonstrate the efficiency and robustness of our approach using Meta-World and ManiSkill3. For project website and code, see https://aalmuzairee.github.io/mad

Seth Farrell, Chenghao Li, Hongzhan Yu et al.

The integration of autonomous mobile robots (AMRs) in industrial environments, particularly warehouses, has revolutionized logistics and operational efficiency. However, ensuring the safety of human workers in dynamic, shared spaces remains a critical challenge. This work proposes a novel methodology that leverages control barrier functions (CBFs) to enhance safety in warehouse navigation. By integrating learning-based CBFs with the Open Robotics Middleware Framework (OpenRMF), the system achieves adaptive and safety-enhanced controls in multi-robot, multi-agent scenarios. Experiments conducted using various robot platforms demonstrate the efficacy of the proposed approach in avoiding static and dynamic obstacles, including human pedestrians. Our experiments evaluate different scenarios in which the number of robots, robot platforms, speed, and number of obstacles are varied, from which we achieve promising performance.

Quan Vuong, Yuzhe Qin, Runlin Guo et al.

We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device. Our system can perform general manipulation tasks such as cloth folding, hammering and 3mm clearance peg in hole. We propose the use of non-Cartesian oblique coordinate frame, dynamic motion scaling and reposition of operator frames to increase the flexibility of our teleoperation system. We hypothesize that lowering the barrier of entry to teleoperation will allow for wider deployment of supervised autonomy system, which will in turn generates realistic datasets that unlock the potential of machine learning for robotic manipulation. Demo of our systems are available online https://sites.google.com/view/manipulation-teleop-with-rgbd

Aayush Naik, Priyam Parashar, Jiaming Hu et al.

The World Robot Summit (WRS) 2020 Assembly Challenge is designed to allow teams to demonstrate how one can build flexible, robust systems for assembly of machined objects. We present our approach to assembly based on integration of machine vision, robust planning and execution using behavior trees and a hierarchy of recovery strategies to ensure robust operation. Our system was selected for the WRS 2020 Assembly Challenge finals based on robust performance in the qualifying rounds. We present the systems approach adopted for the challenge.

Priyam Parashar, Aayush Naik, Jiaming Hu et al.

The World Robotics Challenge (2018 & 2020) was designed to challenge teams to design systems that are easy to adapt to new tasks and to ensure robust operation in a semi-structured environment. We present a layered strategy to transform missions into tasks and actions and provide a set of strategies to address simple and complex failures. We propose a model for characterizing failures using this model and discuss repairs. Simple failures are by far the most common in our WRC system and we also present how we repaired them.

David Paz, Hengyuan Zhang, Henrik I. Christensen

In recent years, various state of the art autonomous vehicle systems and architectures have been introduced. These methods include planners that depend on high-definition (HD) maps and models that learn an autonomous agent's controls in an end-to-end fashion. While end-to-end models are geared towards solving the scalability constraints from HD maps, they do not generalize for different vehicles and sensor configurations. To address these shortcomings, we introduce an approach that leverages lightweight map representations, explicitly enforcing geometric constraints, and learns feasible trajectories using a conditional generative model. Additional contributions include a new dataset that is used to verify our proposed models quantitatively. The results indicate low relative errors that can potentially translate to traversable trajectories. The dataset created as part of this work has been made available online.

Henrik Christensen, Maria Gini, Odest Chadwicke Jenkins et al.

Robotics has the potential to magnify the skilled workforce of the nation by complementing our workforce with automation: teams of people and robots will be able to do more than either could alone. The economic engine of the U.S. runs on the productivity of our people. The rise of automation offers new opportunities to enhance the work of our citizens and drive the innovation and prosperity of our industries. Most critically, we need research to understand how future robot technologies can best complement our workforce to get the best of both human and automated labor in a collaborative team. Investments made in robotics research and workforce development will lead to increased GDP, an increased export-import ratio, a growing middle class of skilled workers, and a U.S.-based supply chain that can withstand global pandemics and other disruptions. In order to make the United States a leader in robotics, we need to invest in basic research, technology development, K-16 education, and lifelong learning.

Jiaming Hu, Hongyi Ling, Priyam Parashar et al.

In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because of the absence of rich texture features. Furthermore, the appearance of specularity heavily depends on the camera viewpoint and environmental light conditions making traditional methods, like template matching, fail. In the last 30 years, pose estimation of the specular object has been a consistent challenge, and most related works require massive knowledge modeling effort for light setups, environment, or the object surface. On the other hand, recent works exhibit the feasibility of 6D pose estimation on a monocular camera with convolutional neural networks(CNNs) however they mostly use opaque objects for evaluation. This paper provides a data-driven solution to estimate the 6D pose of specular objects for grasping them, proposes a cost function for handling symmetry, and demonstrates experimental results showing the system's feasibility.

Yunhai Han, Yuhan Liu, David Paz et al.

Calibration of sensors is fundamental to robust performance for intelligent vehicles. In natural environments, disturbances can easily challenge calibration. One possibility is to use natural objects of known shape to recalibrate sensors. An approach based on recognition of traffic signs, such as stop signs, and use of them for recalibration of cameras is presented. The approach is based on detection, geometry estimation, calibration, and recursive updating. Results from natural environments are presented that clearly show convergence and improved performance.

David Paz, Hengyuan Zhang, Qinru Li et al.

Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate. While many of the architectures previously introduced are capable of operating under highly dynamic environments, many of these are constrained to smaller-scale deployments, require constant maintenance due to the associated scalability cost with high-definition (HD) maps, and involve tedious manual labeling. As an attempt to tackle this problem, we propose to fuse image and pre-built point cloud map information to perform automatic and accurate labeling of static landmarks such as roads, sidewalks, crosswalks, and lanes. The method performs semantic segmentation on 2D images, associates the semantic labels with point cloud maps to accurately localize them in the world, and leverages the confusion matrix formulation to construct a probabilistic semantic map in bird's eye view from semantic point clouds. Experiments from data collected in an urban environment show that this model is able to predict most road features and can be extended for automatically incorporating road features into HD maps with potential future work directions.

David Paz, Po-jung Lai, Nathan Chan et al.

With the recent development of autonomous vehicle technology, there have been active efforts on the deployment of this technology at different scales that include urban and highway driving. While many of the prototypes showcased have been shown to operate under specific cases, little effort has been made to better understand their shortcomings and generalizability to new areas. Distance, uptime and number of manual disengagements performed during autonomous driving provide a high-level idea on the performance of an autonomous system but without proper data normalization, testing location information, and the number of vehicles involved in testing, the disengagement reports alone do not fully encompass system performance and robustness. Thus, in this study a complete set of metrics are applied for benchmarking autonomous vehicle systems in a variety of scenarios that can be extended for comparison with human drivers and other autonomous vehicle systems. These metrics have been used to benchmark UC San Diego's autonomous vehicle platforms during early deployments for micro-transit and autonomous mail delivery applications.

Yiding Qiu, Anwesan Pal, Henrik I. Christensen

Direct search for objects as part of navigation poses a challenge for small items. Utilizing context in the form of object-object relationships enable hierarchical search for targets efficiently. Most of the current approaches tend to directly incorporate sensory input into a reward-based learning approach, without learning about object relationships in the natural environment, and thus generalize poorly across domains. We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR), a target-driven navigation algorithm, which considers the inherent relationship between target objects, and the more salient contextual objects occurring in its surrounding. Extensive experiments conducted across multiple environment settings show an $82.9\%$ and $93.5\%$ gain over existing state-of-the-art navigation methods in terms of the success rate (SR), and success weighted by path length (SPL), respectively. We also show that our model learns to converge much faster than other algorithms, without suffering from the well-known overfitting problem. Additional details regarding the supplementary material and code are available at https://sites.google.com/eng.ucsd.edu/mjolnir.

Anwesan Pal, Sayan Mondal, Henrik I. Christensen

In recent years, predicting driver's focus of attention has been a very active area of research in the autonomous driving community. Unfortunately, existing state-of-the-art techniques achieve this by relying only on human gaze information, thereby ignoring scene semantics. We propose a novel Semantics Augmented GazE (SAGE) detection approach that captures driving specific contextual information, in addition to the raw gaze. Such a combined attention mechanism serves as a powerful tool to focus on the relevant regions in an image frame in order to make driving both safe and efficient. Using this, we design a complete saliency prediction framework - SAGE-Net, which modifies the initial prediction from SAGE by taking into account vital aspects such as distance to objects (depth), ego vehicle speed, and pedestrian crossing intent. Exhaustive experiments conducted through four popular saliency algorithms show that on $\mathbf{49/56\text{ }(87.5\%)}$ cases - considering both the overall dataset and crucial driving scenarios, SAGE outperforms existing techniques without any additional computational overhead during the training process. The augmented dataset along with the relevant code are available as part of the supplementary material.

Jiachen Li, Quan Vuong, Shuang Liu et al.

We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks sampled from the same distribution. The task identities of the unseen tasks are not provided. To perform well, the policy must infer the task identity from collected transitions by modelling its dependency on states, actions and rewards. Because the different datasets may have state-action distributions with large divergence, the task inference module can learn to ignore the rewards and spuriously correlate $\textit{only}$ state-action pairs to the task identity, leading to poor test time performance. To robustify task inference, we propose a novel application of the triplet loss. To mine hard negative examples, we relabel the transitions from the training tasks by approximating their reward functions. When we allow further training on the unseen tasks, using the trained policy as an initialization leads to significantly faster convergence compared to randomly initialized policies (up to $80\%$ improvement and across 5 different Mujoco task distributions). We name our method $\textbf{MBML}$ ($\textbf{M}\text{ulti-task}$ $\textbf{B}\text{atch}$ RL with $\textbf{M}\text{etric}$ $\textbf{L}\text{earning}$).

Ruffin White, Gianluca Caiazza, Chenxu Jiang et al.

Distribution Service (DDS) is a realtime peer-to-peer protocol that serves as a scalable middleware between distributed networked systems found in many Industrial IoT domains such as automotive, medical, energy, and defense. Since the initial ratification of the standard, specifications have introduced a Security Model and Service Plugin Interface (SPI) architecture, facilitating authenticated encryption and data centric access control while preserving interoperable data exchange. However, as Secure DDS v1.1, the default plugin specifications presently exchanges digitally signed capability lists of both participants in the clear during the crypto handshake for permission attestation; thus breaching confidentiality of the context of the connection. In this work, we present an attacker model that makes use of network reconnaissance afforded by this leaked context in conjunction with formal verification and model checking to arbitrarily reason about the underlying topology and reachability of information flow, enabling targeted attacks such as selective denial of service, adversarial partitioning of the data bus, or vulnerability excavation of vendor implementations.

Quan Vuong, Sharad Vikram, Hao Su et al.

Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input. However, most of this success is limited to simulation. While there are promising successes in applying RL algorithms directly on real systems, their performance on more complex systems remains bottle-necked by the relative data inefficiency of RL algorithms. Domain randomization is a promising direction of research that has demonstrated impressive results using RL algorithms to control real robots. At a high level, domain randomization works by training a policy on a distribution of environmental conditions in simulation. If the environments are diverse enough, then the policy trained on this distribution will plausibly generalize to the real world. A human-specified design choice in domain randomization is the form and parameters of the distribution of simulated environments. It is unclear how to the best pick the form and parameters of this distribution and prior work uses hand-tuned distributions. This extended abstract demonstrates that the choice of the distribution plays a major role in the performance of the trained policies in the real world and that the parameter of this distribution can be optimized to maximize the performance of the trained policies in the real world

Rahul Sawhney, Fuxin Li, Henrik I. Christensen et al.

We address the problems of measuring geometric similarity between 3D scenes, represented through point clouds or range data frames, and associating them. Our approach leverages macro-scale 3D structural geometry - the relative configuration of arbitrary surfaces and relationships among structures that are potentially far apart. We express such discriminative information in a viewpoint-invariant feature space. These are subsequently encoded in a frame-level signature that can be utilized to measure geometric similarity. Such a characterization is robust to noise, incomplete and partially overlapping data besides viewpoint changes. We show how it can be employed to select a diverse set of data frames which have structurally similar content, and how to validate whether views with similar geometric content are from the same scene. The problem is formulated as one of general purpose retrieval from an unannotated, spatio-temporally unordered database. Empirical analysis indicates that the presented approach thoroughly outperforms baselines on depth / range data. Its depth-only performance is competitive with state-of-the-art approaches with RGB or RGB-D inputs, including ones based on deep learning. Experiments show retrieval performance to hold up well with much sparser databases, which is indicative of the approach's robustness. The approach generalized well - it did not require dataset specific training, and scaled up in our experiments. Finally, we also demonstrate how geometrically diverse selection of views can result in richer 3D reconstructions.

Steven Hickson, Stan Birchfield, Irfan Essa et al.

We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach. Our algorithm processes a moving window over several point clouds to group similar regions over a graph, resulting in an initial over-segmentation. These regions are then merged to yield a dendrogram using agglomerative clustering via a minimum spanning tree algorithm. Bipartite graph matching at a given level of the hierarchical tree yields the final segmentation of the point clouds by maintaining region identities over arbitrarily long periods of time. We show that a multistage segmentation with depth then color yields better results than a linear combination of depth and color. Due to its incremental processing, our algorithm can process videos of any length and in a streaming pipeline. The algorithm's ability to produce robust, efficient segmentation is demonstrated with numerous experimental results on challenging sequences from our own as well as public RGBD data sets.

Akansel Cosgun, Henrik Christensen

We discuss the process of building semantic maps, how to interactively label entities in them, and how to use them to enable context-aware navigation behaviors in human environments. We utilize planar surfaces, such as walls and tables, and static objects, such as door signs, as features for our semantic mapping approach. Users can interactively annotate these features by having the robot follow him/her, entering the label through a mobile app, and performing a pointing gesture toward the landmark of interest. Our gesture based approach can reliably estimate which object is being pointed at and detect ambiguous gestures with probabilistic modeling. Our person following method attempts to maximize future utility by a search for future actions assuming constant velocity model for the human. We describe a method to extract metric goals from a semantic map landmark and to plan a human aware path that takes into account the personal spaces of people. Finally, we demonstrate context-awareness for person following in two scenarios: interactive labeling and door passing. We believe that future navigation approaches and service robotics applications can be made more effective by further exploiting the structure of human environments.

Siddharth Choudhary, Luca Carlone, Carlos Nieto et al.

We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of the two-stage approach of Carlone et al., where we use the Successive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as workhorses to split the computation among the robots. As a second contribution, we extend %and demonstrate the applicability of the proposed distributed algorithms to work with object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g., point clouds) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS requires minimal information exchange, has an anytime flavor, scales well to large teams, is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems.

Ruffin White, Dr. Henrik I. Christensen, Dr. Morgan Quigley

SROS is a proposed addition to the ROS API and ecosystem to support modern cryptography and security measures. An overview of current progress will be presented, rationalizing each major advancement, including: over-the-wire cryptography for all data transport, namespaced access control enforcing graph policies/restrictions, and finally process profiles using Linux Security Modules to harden a node's resource access. By making the community aware of the vulnerabilities in ROS, as well as the proposed solutions provided by SROS, we intend to improve the state of security for future robotics subsystems.

Samarth Brahmbhatt, Henrik I. Christensen, James Hays

We propose a Convolutional Neural Network (CNN) based algorithm - StuffNet - for object detection. In addition to the standard convolutional features trained for region proposal and object detection [31], StuffNet uses convolutional features trained for segmentation of objects and 'stuff' (amorphous categories such as ground and water). Through experiments on Pascal VOC 2010, we show the importance of features learnt from stuff segmentation for improving object detection performance. StuffNet improves performance from 18.8% mAP to 23.9% mAP for small objects. We also devise a method to train StuffNet on datasets that do not have stuff segmentation labels. Through experiments on Pascal VOC 2007 and 2012, we demonstrate the effectiveness of this method and show that StuffNet also significantly improves object detection performance on such datasets.

Henrik I Christensen, Allison Okamura, Maja Mataric et al.

The National Robotics Initiative (NRI) was launched 2011 and is about to celebrate its 5 year anniversary. In parallel with the NRI, the robotics community, with support from the Computing Community Consortium, engaged in a series of road mapping exercises. The first version of the roadmap appeared in September 2009; a second updated version appeared in 2013. While not directly aligned with the NRI, these road-mapping documents have provided both a useful charting of the robotics research space, as well as a metric by which to measure progress. This report sets forth a perspective of progress in robotics over the past five years, and provides a set of recommendations for the future. The NRI has in its formulation a strong emphasis on co-robot, i.e., robots that work directly with people. An obvious question is if this should continue to be the focus going forward? To try to assess what are the main trends, what has happened the last 5 years and what may be promising directions for the future a small CCC sponsored study was launched to have two workshops, one in Washington DC (March 5th, 2016) and another in San Francisco, CA (March 11th, 2016). In this report we brief summarize some of the main discussions and observations from those workshops. We will present a variety of background information in Section 2, and outline various issues related to progress over the last 5 years in Section 3. In Section 4 we will outline a number of opportunities for moving forward. Finally, we will summarize the main points in Section 5.

Ana Huaman Quispe, Heni Ben Amor, Henrik Christensen et al.

In this paper we propose an approach for efficient grasp selection for manipulation tasks of unknown objects. Even for simple tasks such as pick-and-place, a unique solution is rare to occur. Rather, multiple candidate grasps must be considered and (potentially) tested till a successful, kinematically feasible path is found. To make this process efficient, the grasps should be ordered such that those more likely to succeed are tested first. We propose to use grasp manipulability as a metric to prioritize grasps. We present results of simulation experiments which demonstrate the usefulness of our metric. Additionally, we present experiments with our physical robot performing simple manipulation tasks with a small set of different household objects.

Prateek Singhal, Ruffin White, Henrik Christensen

We present an on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from visual odometry. Using a combination of vision and odometry that are tightly integrated we can increase the overall performance of object based tracking for semantic mapping. We present a framework for integration of the two data-sources into a coherent framework through information based fusion/arbitration. We demonstrate the framework in the context of OmniMapper[1] and present results on 6 challenging sequences over multiple objects compared to data obtained from a motion capture systems. We are able to achieve a mean error of 0.23m for per frame tracking showing 9% relative error less than state of the art tracker.

Daniel Castro, Steven Hickson, Vinay Bettadapura et al.

We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.

Samarth Brahmbhatt, Heni Ben Amor, Henrik Christensen

We present a learning approach for localization and segmentation of objects in an image in a manner that is robust to partial occlusion. Our algorithm produces a bounding box around the full extent of the object and labels pixels in the interior that belong to the object. Like existing segmentation aware detection approaches, we learn an appearance model of the object and consider regions that do not fit this model as potential occlusions. However, in addition to the established use of pairwise potentials for encouraging local consistency, we use higher order potentials which capture information at the level of im- age segments. We also propose an efficient loss function that targets both localization and segmentation performance. Our algorithm achieves 13.52% segmentation error and 0.81 area under the false-positive per image vs. recall curve on average over the challenging CMU Kitchen Occlusion Dataset. This is a 42.44% decrease in segmentation error and a 16.13% increase in localization performance compared to the state-of-the-art. Finally, we show that the visibility labelling produced by our algorithm can make full 3D pose estimation from a single image robust to occlusion.

Rahul Sawhney, Henrik I. Christensen, Gary R. Bradski

Mean Shift today, is widely used for mode detection and clustering. The technique though, is challenged in practice due to assumptions of isotropicity and homoscedasticity. We present an adaptive Mean Shift methodology that allows for full anisotropic clustering, through unsupervised local bandwidth selection. The bandwidth matrices evolve naturally, adapting locally through agglomeration, and in turn guiding further agglomeration. The online methodology is practical and effecive for low-dimensional feature spaces, preserving better detail and clustering salience. Additionally, conventional Mean Shift either critically depends on a per instance choice of bandwidth, or relies on offline methods which are inflexible and/or again data instance specific. The presented approach, due to its adaptive design, also alleviates this issue - with a default form performing generally well. The methodology though, allows for effective tuning of results.

Rahul Sawhney, Fuxin Li, Henrik I. Christensen

A fundamental challenge to sensory processing tasks in perception and robotics is the problem of obtaining data associations across views. We present a robust solution for ascertaining potentially dense surface patch (superpixel) associations, requiring just range information. Our approach involves decomposition of a view into regularized surface patches. We represent them as sequences expressing geometry invariantly over their superpixel neighborhoods, as uniquely consistent partial orderings. We match these representations through an optimal sequence comparison metric based on the Damerau-Levenshtein distance - enabling robust association with quadratic complexity (in contrast to hitherto employed joint matching formulations which are NP-complete). The approach is able to perform under wide baselines, heavy rotations, partial overlaps, significant occlusions and sensor noise. The technique does not require any priors -- motion or otherwise, and does not make restrictive assumptions on scene structure and sensor movement. It does not require appearance -- is hence more widely applicable than appearance reliant methods, and invulnerable to related ambiguities such as textureless or aliased content. We present promising qualitative and quantitative results under diverse settings, along with comparatives with popular approaches based on range as well as RGB-D data.