Jun Jin

Xirui Shi, Jun Jin

We consider the problem of using diffusion models to generate fast, smooth, and temporally consistent robot motions. Although diffusion models have demonstrated superior performance in robot learning due to their task scalability and multi-modal flexibility, they suffer from two fundamental limitations: (1) they often produce non-smooth, jerky motions due to their inability to capture temporally consistent movement dynamics, and (2) their iterative sampling process incurs prohibitive latency for many robotic tasks. Inspired by classic robot motion generation methods such as DMPs and ProMPs, which capture temporally and spatially consistent dynamic of trajectories using low-dimensional vectors -- and by recent advances in diffusion-based image generation that use consistency models with probability flow ODEs to accelerate the denoising process, we propose Fast Robot Motion Diffusion (FRMD). FRMD uniquely integrates Movement Primitives (MPs) with Consistency Models to enable efficient, single-step trajectory generation. By leveraging probabilistic flow ODEs and consistency distillation, our method models trajectory distributions while learning a compact, time-continuous motion representation within an encoder-decoder architecture. This unified approach eliminates the slow, multi-step denoising process of conventional diffusion models, enabling efficient one-step inference and smooth robot motion generation. We extensively evaluated our FRMD on the well-recognized Meta-World and ManiSkills Benchmarks, ranging from simple to more complex manipulation tasks, comparing its performance against state-of-the-art baselines. Our results show that FRMD generates significantly faster, smoother trajectories while achieving higher success rates.

Yao Mu, Qinglong Zhang, Mengkang Hu et al.

Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset.

Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh et al.

We propose a multi-time-scale predictive representation learning method to efficiently learn robust driving policies in an offline manner that generalize well to novel road geometries, and damaged and distracting lane conditions which are not covered in the offline training data. We show that our proposed representation learning method can be applied easily in an offline (batch) reinforcement learning setting demonstrating the ability to generalize well and efficiently under novel conditions compared to standard batch RL methods. Our proposed method utilizes training data collected entirely offline in the real-world which removes the need of intensive online explorations that impede applying deep reinforcement learning on real-world robot training. Various experiments were conducted in both simulator and real-world scenarios for the purpose of evaluation and analysis of our proposed claims.

Laura Petrich, Jun Jin, Masood Dehghan et al.

Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers present bottom up solutions using various control methods for different types of movements. Health research on the other hand focuses on clinical assessment and rehabilitation leaving arguably important differences between the two domains. In particular, little is known quantitatively on what ADLs humans perform in their everyday environment - at home, work etc. This information can help guide development and prioritization of robotic technology for in-home assistive robotic deployment. This study targets several lifelogging databases, where we compute (i) ADL task frequency from long-term low sampling frequency video and Internet of Things (IoT) sensor data, and (ii) short term arm and hand movement data from 30 fps video data of domestic tasks. Robotics and health care communities have different terms and taxonomies for representing tasks and motions. We derive and discuss a robotics-relevant taxonomy from this quantitative ADL task and ICF motion data in attempt to ameliorate these taxonomic differences. Our statistics quantify that humans reach, open drawers, doors, and retrieve and use objects hundreds of times a day. Commercial wheelchair mounted robot arms can help 150,000 upper body disabled in the USA alone, but only a few hundred robots are deployed. Better user interfaces, and more capable robots can increase the potential user base and number of ADL tasks solved significantly.

Daniel Graves, Jun Jin, Jun Luo

We propose a framework for transferring any existing policy from a potentially unknown source MDP to a target MDP. This framework (1) enables reuse in the target domain of any form of source policy, including classical controllers, heuristic policies, or deep neural network-based policies, (2) attains optimality under suitable theoretical conditions, and (3) guarantees improvement over the source policy in the target MDP. These are achieved by packaging the source policy as a black-box option in the target MDP and providing a theoretically grounded way to learn the option's initiation set through general value functions. Our approach facilitates the learning of new policies by (1) maximizing the target MDP reward with the help of the black-box option, and (2) returning the agent to states in the learned initiation set of the black-box option where it is already optimal. We show that these two variants are equivalent in performance under some conditions. Through a series of experiments in simulated environments, we demonstrate that our framework performs excellently in sparse reward problems given (sub-)optimal source policies and improves upon prior art in transfer methods such as continual learning and progressive networks, which lack our framework's desirable theoretical properties.

Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh et al.

Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits in performance and generalization are observed on unseen test roads in both simulation and on a real Jackal robot. The novel predictive representation is learned by general value functions (GVFs) to provide out-of-policy, or counter-factual, predictions of future lane centeredness and road angle that form a compact representation of the state of the agent improving learning in both online and offline reinforcement learning to learn to drive an autonomous vehicle with methods that generalizes well to roads not in the training data. Experiments in both simulation and the real-world demonstrate that predictive representations in reinforcement learning improve learning efficiency, smoothness of control and generalization to roads that the agent was never shown during training, including damaged lane markings. It was found that learning a predictive representation that consists of several predictions over different time scales, or discount factors, improves the performance and smoothness of the control substantially. The Jackal robot was trained in a two step process where the predictive representation is learned first followed by a batch reinforcement learning algorithm (BCQ) from data collected through both automated and human-guided exploration in the environment. We conclude that out-of-policy predictive representations with GVFs offer reinforcement learning many benefits in real-world problems.

Jun Jin, Laura Petrich, Masood Dehghan et al.

We consider the problem of visual imitation learning without human supervision (e.g. kinesthetic teaching or teleoperation), nor access to an interactive reinforcement learning (RL) training environment. We present a geometric perspective to derive solutions to this problem. Specifically, we propose VGS-IL (Visual Geometric Skill Imitation Learning), an end-to-end geometry-parameterized task concept inference method, to infer globally consistent geometric feature association rules from human demonstration video frames. We show that, instead of learning actions from image pixels, learning a geometry-parameterized task concept provides an explainable and invariant representation across demonstrator to imitator under various environmental settings. Moreover, such a task concept representation provides a direct link with geometric vision based controllers (e.g. visual servoing), allowing for efficient mapping of high-level task concepts to low-level robot actions.

Jun Jin, Nhat M. Nguyen, Nazmus Sakib et al.

We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot's perspective while social-safety to measure the impact of our robot's actions on surrounding pedestrians. Specifically, the social-safety part predicts the intrusion impact of our robot's action into the interaction area with surrounding humans. We train the policy using reinforcement learning on a simple simulator and directly evaluate the learned policy in Gazebo and real robot tests. Experiments show the learned policy can be smoothly transferred without any fine tuning. We observe that our method demonstrates time-efficient path planning behavior with high success rate in mapless navigation tasks. Furthermore, we test our method in a navigation among dynamic crowds task considering both low and high volume traffic. Our learned policy demonstrates cooperative behavior that actively drives our robot into traffic flows while showing respect to nearby pedestrians. Evaluation videos are at https://sites.google.com/view/ssw-batman

Jun Jin, Laura Petrich, Shida He et al.

We present a coarse-to-fine approach based semi-autonomous teleoperation system using vision guidance. The system is optimized for long range teleoperation tasks under time-delay network conditions and does not require prior knowledge of the remote scene. Our system initializes with a self exploration behavior that senses the remote surroundings through a freely mounted eye-in-hand web cam. The self exploration stage estimates hand-eye calibration and provides a telepresence interface via real-time 3D geometric reconstruction. The human operator is able to specify a visual task through the interface and a coarse-to-fine controller guides the remote robot enabling our system to work in high latency networks. Large motions are guided by coarse 3D estimation, whereas fine motions use image cues (IBVS). Network data transmission cost is minimized by sending only sparse points and a final image to the human side. Experiments from Singapore to Canada on multiple tasks were conducted to show our system's capability to work in long range teleoperation tasks.

Jun Jin, Masood Dehghan, Laura Petrich et al.

We evaluate different state representation methods in robot hand-eye coordination learning on different aspects. Regarding state dimension reduction: we evaluates how these state representation methods capture relevant task information and how much compactness should a state representation be. Regarding controllability: experiments are designed to use different state representation methods in a traditional visual servoing controller and a REINFORCE controller. We analyze the challenges arisen from the representation itself other than from control algorithms. Regarding embodiment problem in LfD: we evaluate different method's capability in transferring learned representation from human to robot. Results are visualized for better understanding and comparison.

Jun Jin, Laura Petrich, Masood Dehghan et al.

We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL) by inferring differential rewards between state changes. The learned task function is then used as continuous feedbacks in an uncalibrated visual servoing(UVS) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. It can also provide task interpretability by directly approximating the task function. Besides, benefiting from the use of a traditional UVS controller, our training process is efficient and the learned policy is independent from a particular robot platform. Various experiments were designed to show that, for a certain DOF task, our method can adapt to task/environment variances in target positions, backgrounds, illuminations, and occlusions without prior retraining.

Masood Dehghan, Zichen Zhang, Mennatullah Siam et al.

This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new tools and motions are taught on the fly. The robotic system developed was one of the five finalists in the KUKA Innovation Award competition and demonstrated during the Hanover Messe 2018 in Germany. The main contributions of the system are a) a novel incremental object learning module - a deep learning based localization and recognition system - that allows a human to teach new objects to the robot, b) an intuitive user interface for specifying 3D motion task associated with the new object, c) a hybrid force-vision control module for performing compliant motion on an unstructured surface. This paper describes the implementation and integration of the main modules of the system and summarizes the lessons learned from the competition.