1 citations · 1 across the 9 of their papers we have counts for
9 papers
Scaling Nonlinear Optimization: Many Problems One GPU
John Viljoen, Johanna Haffner, Masayoshi Tomizuka +1
Many robotics problems, including trajectory optimization, inverse kinematics, and contact-rich motion planning, reduce to nonlinear programs (NLPs). Mature NLP solvers such as IPO…
X-Safe: Embodiment-Agnostic Safeguarding for Robot Arm Manipulation Policies via Action Masking
Alex Beaudin, Hanna Krasowski, Kartik Nagpal +3
Ensuring safety of learning-enabled robotic manipulation across diverse embodiments and tasks still requires significant manual engineering. For collision avoidance, existing appro…
Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment
Kartik Nagpal, Dayi Dong, Jean-Baptiste Bouvier +1
Recent work, spanning from autonomous vehicle coordination to in-space assembly, has shown the importance of learning collaborative behavior for enabling robots to achieve shared g…
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems
Jean-Baptiste Bouvier, Kartik Nagpal, Negar Mehr
In this paper, we develop a method for learning a control policy guaranteed to satisfy an affine state constraint of high relative degree in closed loop with a black-box system. Pr…
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
Rohan Chandra, Haresh Karnan, Negar Mehr +2
Social robot navigation in crowded public spaces such as university campuses, restaurants, grocery stores, and hospitals, is an increasingly important area of research. One of the…
Integrating Predictive Motion Uncertainties with Distributionally Robust Risk-Aware Control for Safe Robot Navigation in Crowds
Kanghyun Ryu, Negar Mehr
Ensuring safe navigation in human-populated environments is crucial for autonomous mobile robots. Although recent advances in machine learning offer promising methods to predict hu…
POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints
Jean-Baptiste Bouvier, Kartik Nagpal, Negar Mehr
In this paper, we seek to learn a robot policy guaranteed to satisfy state constraints. To encourage constraint satisfaction, existing RL algorithms typically rely on Constrained M…
Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
Gokul Puthumanaillam, Xiangyu Liu, Negar Mehr +1
Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments. Environmental variability over tim…
Optimal Robotic Assembly Sequence Planning: A Sequential Decision-Making Approach
Kartik Nagpal, Negar Mehr
The optimal robot assembly planning problem is challenging due to the necessity of finding the optimal solution amongst an exponentially vast number of possible plans, all while sa…