Neeloy Chakraborty

Shuijing Liu, Peixin Chang, Zhe Huang et al.

We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. To learn a safe and efficient robot policy, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time. To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several timesteps. The predictions are incorporated into a model-free RL framework to prevent the robot from intruding into the intended paths of other agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios. We successfully transfer the policy learned in simulation to a real-world TurtleBot 2i. Our code and videos are available at https://sites.google.com/view/intention-aware-crowdnav/home.

Aamir Hasan, Neeloy Chakraborty, Haonan Chen et al.

Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle fleets, and are hence limited in practice as they fail to account for uncertainty in human behavior. Piecewise Constant (PC) Policies address these issues by structurally modeling the likeness of human driving to reduce traffic congestion in dense scenarios to provide action advice to be followed by human drivers. However, PC policies assume that all drivers behave similarly. To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP. PeRP advises drivers to behave in ways that mitigate traffic congestion. We first infer the driver's intrinsic traits on how they follow instructions in an unsupervised manner with a variational autoencoder. Then, a policy conditioned on the inferred trait adapts the action of the PC policy to provide the driver with a personalized recommendation. Our system is trained in simulation with novel driver modeling of instruction adherence. We show that our approach successfully mitigates congestion while adapting to different driver behaviors, with 4 to 22% improvement in average speed over baselines.

Aamir Hasan, Neeloy Chakraborty, Cathy Wu et al.

The effects of traffic congestion are widespread and are an impedance to everyday life. Piecewise constant driving policies have shown promise in helping mitigate traffic congestion in simulation environments. However, no works currently test these policies in situations involving real human users. Thus, we propose to evaluate these policies through the use of a shared control framework in a collaborative experiment with the human driver and the driving policy aiming to co-operatively mitigate congestion. We intend to use the CARLA simulator alongside the Flow framework to conduct user studies to evaluate the affect of piecewise constant driving policies. As such, we present our in-progress work in building our framework and discuss our proposed plan on evaluating this framework through a human-in-the-loop simulation user study.

Neeloy Chakraborty, Yixiao Fang, Andre Schreiber et al.

Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot's actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, ours is one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Resulting videos and code are provided at https://sites.google.com/illinois.edu/comp-teleop.

Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell

Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture, healthcare, entertainment, and other industries. Most of these systems are developed with modular sub-components for decision-making, planning, and control that may be hand-engineered or learning-based. While these approaches perform well under the situations they were specifically designed for, they can perform especially poorly in out-of-distribution scenarios that will undoubtedly arise at test-time. The rise of foundation models trained on multiple tasks with impressively large datasets has led researchers to believe that these models may provide "common sense" reasoning that existing planners are missing, bridging the gap between algorithm development and deployment. While researchers have shown promising results in deploying foundation models to decision-making tasks, these models are known to hallucinate and generate decisions that may sound reasonable, but are in fact poor. We argue there is a need to step back and simultaneously design systems that can quantify the certainty of a model's decision, and detect when it may be hallucinating. In this work, we discuss the current use cases of foundation models for decision-making tasks, provide a general definition for hallucinations with examples, discuss existing approaches to hallucination detection and mitigation with a focus on decision problems, present guidelines, and explore areas for further research in this exciting field.

Shuijing Liu, Haochen Xia, Fatemeh Cheraghi Pouria et al.

We study the problem of robot navigation in dense and interactive crowds with environmental constraints such as corridors and furniture. Previous methods fail to consider all types of interactions among agents and obstacles, leading to unsafe and inefficient robot paths. In this article, we leverage a graph-based representation of crowded and constrained scenarios and propose a structured framework to learn robot navigation policies with deep reinforcement learning. We first split the representations of different components in the environment and propose a heterogeneous spatio-temporal (st) graph to model distinct interactions among humans, robots, and obstacles. Based on the heterogeneous st-graph, we propose HEIGHT, a novel navigation policy network architecture with different components to capture heterogeneous interactions among entities through space and time. HEIGHT utilizes attention mechanisms to prioritize important interactions and a recurrent network to track changes in the dynamic scene over time, encouraging the robot to avoid collisions adaptively. Through extensive simulation and real-world experiments, we demonstrate that HEIGHT outperforms state-of-the-art baselines in terms of success and efficiency in challenging navigation scenarios. Furthermore, we demonstrate that our pipeline achieves better zero-shot generalization capability than previous works when the densities of humans and obstacles change. More videos are available at https://sites.google.com/view/crowdnav-height/home.

Tianchen Ji, Neeloy Chakraborty, Andre Schreiber et al.

As automated vehicles enter public roads, safety in a near-infinite number of driving scenarios becomes one of the major concerns for the widespread adoption of fully autonomous driving. The ability to detect anomalous situations outside of the operational design domain is a key component in self-driving cars, enabling us to mitigate the impact of abnormal ego behaviors and to realize trustworthy driving systems. On-road anomaly detection in egocentric videos remains a challenging problem due to the difficulties introduced by complex and interactive scenarios. We conduct a holistic analysis of common on-road anomaly patterns, from which we propose three unsupervised anomaly detection experts: a scene expert that focuses on frame-level appearances to detect abnormal scenes and unexpected scene motions; an interaction expert that models normal relative motions between two road participants and raises alarms whenever anomalous interactions emerge; and a behavior expert which monitors abnormal behaviors of individual objects by future trajectory prediction. To combine the strengths of all the modules, we propose an expert ensemble (Xen) using a Kalman filter, in which the final anomaly score is absorbed as one of the states and the observations are generated by the experts. Our experiments employ a novel evaluation protocol for realistic model performance, demonstrate superior anomaly detection performance than previous methods, and show that our framework has potential in classifying anomaly types using unsupervised learning on a large-scale on-road anomaly dataset.

Neeloy Chakraborty, John Pohovey, Melkior Ornik et al.

Large language models (LLMs) have recently demonstrated success in generalizing across decision-making tasks including planning, control, and prediction, but their tendency to hallucinate unsafe and undesired outputs poses risks. We argue that detecting such failures is necessary, especially in safety-critical scenarios. Existing methods for black-box models often detect hallucinations by identifying inconsistencies across multiple samples. Many of these approaches typically introduce prompt perturbations like randomizing detail order or generating adversarial inputs, with the intuition that a confident model should produce stable outputs. We first perform a manual case study showing that other forms of perturbations (e.g., adding noise, removing sensor details) cause LLMs to hallucinate in a multi-agent driving environment. We then propose a novel method for efficiently searching the space of prompt perturbations using adaptive stress testing (AST) with Monte-Carlo tree search (MCTS). Our AST formulation enables discovery of scenarios and prompts that cause language models to act with high uncertainty or even crash. By generating MCTS prompt perturbation trees across diverse scenarios, we show through extensive experiments that offline analyses can be used at runtime to automatically generate prompts that influence model uncertainty, and to inform real-time trust assessments of an LLM. We further characterize LLMs deployed as planners in a single-agent lunar lander environment and in a multi-agent robot crowd navigation simulation. Overall, ours is one of the first hallucination intervention algorithms to pave a path towards rigorous characterization of black-box LLM planners.

Aamir Hasan, Neeloy Chakraborty, Haonan Chen et al.

Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over autonomous vehicle fleets, incur extensive installation costs for a centralized sensor ecosystem, and also fail to account for uncertainty in driver behavior. To this end, we develop a class of learned residual policies that can be used in cooperative advisory systems and only require the use of a single vehicle with a human driver. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors, particularly drivers' reactions to instructions, to provide an improved user experience. To realize such policies, we introduce an improved reward function that explicitly addresses congestion mitigation and driver attitudes to advice. We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers. Our results show that our approaches successfully mitigate congestion while adapting to different driver behaviors, with up to 20% and 40% improvement as measured by a combination metric of speed and deviations in speed across time over baselines in our simulation tests and user study, respectively. Our user study further shows that our policies are human-compatible and personalize to drivers.

Aamir Hasan, Neeloy Chakraborty, Haonan Chen et al.

Real-time Advisory (RTA) systems, such as navigational and eco-driving assistants, are becoming increasingly ubiquitous in vehicles due to their benefits for users and society. Until autonomous vehicles mature, such advisory systems will continue to expand their ability to cooperate with drivers, enabling safer and more eco-friendly driving practices while improving user experience. However, the interactions between these systems and drivers have not been studied extensively. To this end, we conduct a driving simulator study (N=16) to capture driver reactions to a Cooperative RTA system. Through a case study with a congestion mitigation assistant, we qualitatively analyze the sentiments of drivers towards advisory systems and discuss driver preferences for various aspects of the interaction. We comment on how the advice should be communicated, the effects of the advice on driver trust, and how drivers adapt to the system. We present recommendations to inform the future design of Cooperative RTA systems.

Shuijing Liu, Peixin Chang, Haonan Chen et al.

Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose an unsupervised method for inferring driver traits such as driving styles from observed vehicle trajectories. We use a variational autoencoder with recurrent neural networks to learn a latent representation of traits without any ground truth trait labels. Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning. Our pipeline enables the autonomous vehicle to adjust its actions when dealing with drivers of different traits to ensure safety and efficiency. Our method demonstrates promising performance and outperforms state-of-the-art baselines in the T-intersection scenario.

Shuijing Liu, Peixin Chang, Weihang Liang et al.

Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous methods deteriorates in partially observable environments and environments with dense crowds. To tackle these problems, we propose decentralized structural-Recurrent Neural Network (DS-RNN), a novel network that reasons about spatial and temporal relationships for robot decision making in crowd navigation. We train our network with model-free deep reinforcement learning without any expert supervision. We demonstrate that our model outperforms previous methods in challenging crowd navigation scenarios. We successfully transfer the policy learned in the simulator to a real-world TurtleBot 2i. For more information, please visit the project website at https://sites.google.com/view/crowdnav-ds-rnn/home.