43 citations · 112 across the 16 of their papers we have counts for
18 papers
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
Lihan Zha, Shresth Grover, Tenny Yin +7
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for c…
LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer
Lihan Zha, Asher J. Hancock, Mingtong Zhang +5
A long-standing goal in robotics is a generalist policy that can be deployed zero-shot on new robot embodiments without per-embodiment adaptation. Despite large-scale multi-embodim…
Guiding Data Collection via Factored Scaling Curves
Lihan Zha, Apurva Badithela, Michael Zhang +7
Generalist imitation learning policies trained on large datasets show great promise for solving diverse manipulation tasks. However, to ensure generalization to different condition…
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
Ola Shorinwa, Zhiting Mei, Justin Lidard +2
The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society.…
Thinking Forward and Backward: Effective Backward Planning with Large Language Models
Allen Z. Ren, Brian Ichter, Anirudha Majumdar
Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities. Most prior work in this area has used LLMs to reason through steps from an initial to a…
Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust
Asher J. Hancock, Allen Z. Ren, Anirudha Majumdar
Vision-language-action (VLA) models trained on large-scale internet data and robot demonstrations have the potential to serve as generalist robot policies. However, despite their l…
Diffusion Policy Policy Optimization
Allen Z. Ren, Justin Lidard, Lars L. Ankile +6
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in conti…
Perceive With Confidence: Statistical Safety Assurances for Navigation with Learning-Based Perception
Zhiting Mei, Anushri Dixit, Meghan Booker +5
Rapid advances in perception have enabled large pre-trained models to be used out of the box for transforming high-dimensional, noisy, and partial observations of the world into ri…
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Allen Z. Ren, Anushri Dixit, Alexandra Bodrova +11
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remai…
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference
Zihan Ding, Yuanpei Chen, Allen Z. Ren +4
Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands. Scripting policies from scratch is intractable due to t…
AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer
Allen Z. Ren, Hongkai Dai, Benjamin Burchfiel +1
Simulation parameter settings such as contact models and object geometry approximations are critical to training robust robotic policies capable of transferring from simulation to…
FlowDrone: Wind Estimation and Gust Rejection on UAVs Using Fast-Response Hot-Wire Flow Sensors
Nathaniel Simon, Allen Z. Ren, Alexander Piqué +4
Unmanned aerial vehicles (UAVs) are finding use in applications that place increasing emphasis on robustness to external disturbances including extreme wind. However, traditional m…
Leveraging Language for Accelerated Learning of Tool Manipulation
Allen Z. Ren, Bharat Govil, Tsung-Yen Yang +2
Robust and generalized tool manipulation requires an understanding of the properties and affordances of different tools. We investigate whether linguistic information about a tool…
Failure Prediction with Statistical Guarantees for Vision-Based Robot Control
Alec Farid, David Snyder, Allen Z. Ren +1
We are motivated by the problem of performing failure prediction for safety-critical robotic systems with high-dimensional sensor observations (e.g., vision). Given access to a bla…
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees
Kai-Chieh Hsu, Allen Z. Ren, Duy Phuong Nguyen +2
Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using rei…
Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data
Abhinav Agarwal, Sushant Veer, Allen Z. Ren +1
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 environme…
Distributionally Robust Policy Learning via Adversarial Environment Generation
Allen Z. Ren, Anirudha Majumdar
Our goal is to train control policies that generalize well to unseen environments. Inspired by the Distributionally Robust Optimization (DRO) framework, we propose DRAGEN - Distrib…
Generalization Guarantees for Imitation Learning
Allen Z. Ren, Sushant Veer, Anirudha Majumdar
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 acc…