28 accepted papers
The integration of foundation models (FMs) into robotics has accelerated real-world deployment, while introducing new safety challenges arising from open-ended semantic reasoning and embodied physical action. These challenges require safety notions beyond physical constraint satisfaction. In this po…
The advent of end-to-end autonomy stacks—often lacking interpretable intermediate modules—has placed an increased burden on ensuring that the final output, i.e., the motion plan, is safe in order to validate the safety of the entire stack. This requires a safety monitor that is both complete (able t…
The advent of end-to-end autonomy stacks—often lacking interpretable intermediate modules—has placed an increased burden on ensuring that the final output, i.e., the motion plan, is safe in order to validate the safety of the entire stack. This requires a safety monitor that is both complete (able t
Learning-based robotic systems demand rigorous validation to assure reliable performance, but extensive real‐world testing is often prohibitively expensive and if conducted may still yield insufficient data for high-confidence guarantees. In this work, we introduce a general estimation framework tha…
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, prior work has mostly explored naive solutions such as fine-tuning, focusing on the motion prediction task. In this work, w
Trajectory prediction, the task of forecasting future agent behavior from past data, is central to safe and efficient autonomous driving. A diverse set of methods (e.g., rule-based or learned with different architectures and datasets) have been proposed, yet it is often the case that the performance
The safety-critical nature of autonomous vehicle (AV) operation necessitates development of task-relevant algorithms that can reason about safety at the system level and not just at the component level. To reason about the impact of a perception failure on the entire system performance, such task-re
Adapting driving behavior to new environments customs and laws is a long-standing problem in autonomous driving precluding the widespread deployment of autonomous vehicles (AVs). In this paper we present LLaDA a simple yet powerful tool that enables human drivers and autonomous vehicles alike to dri…
In highly interactive driving scenarios, the actions of one agent greatly influence those of its neighbors. Planning safe motions for autonomous vehicles (AVs) in such interactive environments, therefore, requires reasoning about the impact of the ego's intended motion plan on nearby agents' behavio
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make vehicles follow specific trajectories and traffic rules. On the other hand, data-driven approaches generate realistic and…
Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to the…
Inductive Conformal Prediction (ICP) provides a practical and effective approach for equipping deep learning models with uncertainty estimates in the form of set-valued predictions which are guaranteed to contain the ground truth with high probability. Despite the appeal of this coverage guarantee,…
Autonomous vehicles must often contend with conflicting planning requirements, e.g., safety and comfort could be at odds with each other if avoiding a collision calls for slamming the brakes. To resolve such conflicts, assigning importance ranking to rules (i.e., imposing a rule hierarchy) has been…
Safety and performance are key enablers for autonomous driving: on the one hand we want our autonomous vehicles (AVs) to be safe, while at the same time their performance (e.g., comfort or progression) is key to adoption. To effectively walk the tightrope between safety and performance, AVs need to…
In this letter, we consider the problem of adapting a dynamically walking bipedal robot to follow a leading co-worker based on physical interaction. Our approach relies on switching among a family of Dynamic Movement Primitives (DMPs) as governed by a supervisor. We train the supervisor to orchestra
We are motivated by the problem of learning policies for robotic systems with rich sensory inputs (e.g., vision) in a manner that allows us to guarantee generalization to environments unseen during training. We provide a framework for providing such generalization guarantees by leveraging a finite d…
In modern autonomy stacks, prediction modules are paramount to planning motions in the presence of other mobile agents. However, failures in prediction modules can mislead the downstream planner into making unsafe decisions. Indeed, the high uncertainty inherent to the task of trajectory forecasting…
Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments that are drawn from a different distribution than the environments used to train the robot. We leverage Probably Approximately Correct (PAC)-Bayes theory in order to train a policy w…
This paper presents an adaptive control scheme for robotic systems that operate in the face of-potentially large-structured uncertainty. The proposed adaptive controller employs an on-line supervisor that utilizes logic-based switching among a finite set of controllers to identify uncertain paramete…
Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert’s policies. In this paper, we present rigorous generalization guarantees for imitation learning
This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Probably Approximately Correct (PAC)-Bayes framework to obtain an upper bound on the expected cost of policies across all env
In this paper, inspired by Proportional-Derivative (PD) control laws, we present a class of Control Lyapunov Function (CLF) based Quadratic Programs (QPs) for robotic systems. Proportional-Derivative (PD) control laws are independent of the robot model, however, they fail to incorporate physical con…
Complex robot motions are frequently generated by composing simpler primitive movements. We use this approach to formulate robot motion plans as sequences of primitives to be executed one after the other. When dealing with dynamical movement primitives, besides accomplishing the high-level objective…
This paper presents a method to achieve online gait adaptation of a dynamically walking biped when collaborating with an external agent-either a human or a robot-acting as a leader. Adaptation occurs without any explicit information on the leader's intended motion; only implicit information is used…
This paper presents a method for navigating 3D dynamically walking bipedal robots amidst obstacles. Our framework relies on composing gait primitives corresponding to limit-cycle locomotion behaviors and it produces nominal motion plans that are compatible with the system's dynamics and can be track…
This paper presents a control method for steering three dimensional (3D) dynamically walking bipeds that are engaged in cooperative tasks such as object transportation. Towards achieving safe interaction with a leading human (or robot) collaborator, the walking biped is required to exhibit complianc…
This paper presents a method for integrating a cooperative manipulation task in the design of dynamic walking motions for an underactuated bipedal robot. Applications that involve physical interaction between a walking biped and a leading human (or robot) collaborator, require that the biped exhibit…
This paper investigates the ability of dynamically walking bipeds to adapt their motion to persistent exogenous forcing. Applications that involve physical interactions between a bipedal robot and other robots (or humans), require that the robot adjust its stepping pattern in response to externally…