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To truly understand how children envision a robot-integrated future, we used personal photo probes and a scaffolded design process to first uncover the values and...
We propose two improvements to Residual RL that further enhance its sample efficiency and make it suitable for stochastic base policies. First, we leverage uncertainty...
We introduce real-is-sim, a new approach to integrating simulation into behavior cloning pipelines. In contrast to real-only methods, our framework allows policies to seamlessly switch...
We propose Q2RL, Q-Estimation and Q-Gating from Behavior Cloning for Reinforcement Learning, an algorithm for efficient offline-to-online learning.
We introduce a multi-task RL training paradigm that treats reference motion as a prior for behavioral shaping rather than a deployment-time constraint.
We introduce Planning-Guided Diffusion Policy Learning (LIDE), an approach that effectively learns to solve contact-rich bimanual manipulation tasks by leveraging model-based motion planners to generate...
We introduce judo, a software package designed to help develop shared tools for prototyping, benchmarking, and deploying sampling-based controllers.
We present the design and analysis of a robotic fingertip equipped with passive rollers that can be selectively braked or pivoted to modulate contact friction...
We present NovaFlow, an autonomous manipulation framework that converts a task description into an actionable plan for a target robot without any demonstrations.
In this study, we investigate how interacting with Boston Dynamics’ Spot, an agile, state-of-the-art quadruped robot, in a public pop-up booth affects perceptions of comfort...
This work demonstrates a front-flip on bicycle robots via reinforcement learning, particularly by imitating reference motions that are infeasible and imperfect. To address this, we...
The Dubins Moving Target Traveling Salesman Problem with Obstacles (Dubins MT-TSP-O) seeks an obstacle-free trajectory for an agent with a fixed speed and minimum turning...
