2 citations · 2 across the 5 of their papers we have counts for
6 papers
Autoregressive Action Sequence Learning for Robotic Manipulation
Xinyu Zhang, Yuhan Liu, Haonan Chang +2
Designing a universal policy architecture that performs well across diverse robots and task configurations remains a key challenge. In this work, we address this by representing ro…
DAP: Diffusion-based Affordance Prediction for Multi-modality Storage
Haonan Chang, Kowndinya Boyalakuntla, Yuhan Liu +3
Solving storage problem: where objects must be accurately placed into containers with precise orientations and positions, presents a distinct challenge that extends beyond traditio…
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
Liam Schramm, Abdeslam Boularias
Monte Carlo tree search (MCTS) has been successful in a variety of domains, but faces challenges with long-horizon exploration when compared to sampling-based motion planning algor…
Bellman Diffusion Models
Liam Schramm, Abdeslam Boularias
Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning a…
USHER: Unbiased Sampling for Hindsight Experience Replay
Liam Schramm, Yunfu Deng, Edgar Granados +1
Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for…
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands
Liam Schramm, Avishai Sintov, Abdeslam Boularias
Transfer learning is a popular approach to bypassing data limitations in one domain by leveraging data from another domain. This is especially useful in robotics, as it allows prac…