160 citations · 403 across the 56 of their papers we have counts for
70 papers
Back on Track: Deployment-time Policy Improvement via World Model Planning
Colton Crosby, Hao Wang, Mac Schwager +1
State-of-the-art visuomotor policies are increasingly capable of executing complex tasks, yet can fail during deployment in ways they cannot predict or recover from on their own. I…
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning
Yusuf Umut Ciftci, Joonwon Kang, Ajay Suresha Sathya +1
Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the dataset. Yet not all states are cre…
Context-Conditioned Hamilton-Jacobi Reachability for Adaptive Safety Filtering
Ali Fuat Sahin, Yunus Yazoglu, John Talbot +3
Hamilton-Jacobi reachability constructs safety certificates for specified dynamics and safety constraints, tying each certificate to the deployment context for which it is synthesi…
When Reasoning Helps Action: Monitoring and Steering Chain-of-Thought in Vision-Language-Action Policies
Sathwik Karnik, Joseph JR. Lee, Aryaman Gupta +1
Reasoning-enabled VLA policies expose chain-of-thought (CoT) traces that appear to explain and guide their actions, creating a potential interface for runtime safety through reason…
When World Models Lie: Adaptive Safety Analysis Under Wrong Imaginations
John Cao, Somil Bansal
World models offer a powerful substrate for safety reasoning in high-dimensional robotic systems, but they are also fallible: their predictions can be biased, miscalibrated, or con…
Hamilton-Jacobi Reachability for Hybrid Systems: Unified Goal-Driven Control with Safety Guarantees
Javier Borquez, Shuang Peng, Somil Bansal
Hybrid dynamical systems provide a powerful modeling framework for robotic systems, particularly in contact-rich environments. However, ensuring safety and performance in such syst…
Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control
Zeyuan Feng, Ali Fuat Sahin, Santiago Thorup +1
Hamilton-Jacobi (HJ) reachability provides a principled framework for synthesizing safety certificates and robust controllers for safety-critical robotic systems. However, applying…
Offline Policy Evaluation for Manipulation Policies via Discounted Liveness Formulation
Hao Wang, Joshua Bowden, Colton Crosby +1
Policy evaluation is a fundamental component of the development and deployment pipeline for robotic policies. In modern manipulation systems, this problem is particularly challengi…
Neural Backward Reach-Avoid Tubes with MPC Supervision for High-Dimensional Systems: An Application to Safe Spacecraft Docking
Santiago Thorup, Luca Castelletto, Zeyuan Feng +1
Autonomous spacecraft docking requires control policies that simultaneously ensure collision avoidance and target reachability under coupled, high-dimensional translational-rotatio…
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Hao Wang, Nam Nguyen, Armand Jordana +2
Autonomous systems are increasingly deployed in real-world environments, where they must achieve high performance while maintaining safety under state and input constraints. Althou…
Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
James Dallas, Thomas Lew, John Talbot +3
Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance…
Steering Away from Memorization: Reachability-Constrained Reinforcement Learning for Text-to-Image Diffusion
Sathwik Karnik, Juyeop Kim, Sanmi Koyejo +2
Text-to-image diffusion models often memorize training data, revealing a fundamental failure to generalize beyond the training set. Current mitigation strategies typically sacrific…
From Words to Safety: Language-Conditioned Safety Filtering for Robot Navigation
Zeyuan Feng, Haimingyue Zhang, Somil Bansal
As robots become increasingly integrated into open-world, human-centered environments, their ability to interpret natural language instructions and adhere to safety constraints is…
Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis
Albert Lin, Alessandro Pinto, Somil Bansal
As perception-based controllers for autonomous systems become increasingly popular in the real world, it is important that we can formally verify their safety and performance despi…
Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks
Hao Wang, Sathwik Karnik, Bea Lim +1
Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their deployment in black-box settings often lea…
MADR: MPC-guided Adversarial DeepReach
Ryan Teoh, Sander Tonkens, William Sharpless +4
Hamilton-Jacobi (HJ) Reachability offers a framework for generating safe value functions and policies in the face of adversarial disturbance, but is limited by the curse of dimensi…
Preemptive Detection and Steering of LLM Misalignment via Latent Reachability
Sathwik Karnik, Somil Bansal
Large language models (LLMs) are now ubiquitous in everyday tools, raising urgent safety concerns about their tendency to generate harmful content. The dominant safety approach --…
MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
Manan Tayal, Aditya Singh, Shishir Kolathaya +1
Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-agent reinforcement learning (MARL), saf…
Safety-Aware Imitation Learning via MPC-Guided Disturbance Injection
Le Qiu, Yusuf Umut Ciftci, Somil Bansal
Imitation Learning has provided a promising approach to learning complex robot behaviors from expert demonstrations. However, learned policies can make errors that lead to safety v…
Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators
Kaustav Chakraborty, Zeyuan Feng, Sushant Veer +6
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 pla…
Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis
Hao Wang, Armand Jordana, Ludovic Righetti +1
While we have made significant algorithmic developments to enable autonomous systems to perform sophisticated tasks, it remains difficult for them to perform tasks effective and sa…
Unsupervised Discovery of Failure Taxonomies from Deployment Logs
Aryaman Gupta, Yusuf Umut Ciftci, Somil Bansal
As robotic systems become increasingly integrated into real-world environments, ranging from autonomous vehicles to household assistants, they inevitably encounter diverse and unst…
Reachability Barrier Networks: Learning Hamilton-Jacobi Solutions for Smooth and Flexible Control Barrier Functions
Matthew Kim, William Sharpless, Hyun Joe Jeong +3
Recent developments in autonomous driving and robotics underscore the necessity of safety-critical controllers. Control barrier functions (CBFs) are a popular method for appending…
Bridging Model Predictive Control and Deep Learning for Scalable Reachability Analysis
Zeyuan Feng, Le Qiu, Somil Bansal
Hamilton-Jacobi (HJ) reachability analysis is a widely used method for ensuring the safety of robotic systems. Traditional approaches compute reachable sets by numerically solving…