Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Schramm, Liam"'
Autoregressive models have demonstrated remarkable success in natural language processing. In this work, we design a simple yet effective autoregressive architecture for robotic manipulation tasks. We propose the Chunking Causal Transformer (CCT), wh
Externí odkaz:
http://arxiv.org/abs/2410.03132
Autor:
Chang, Haonan, Boyalakuntla, Kowndinya, Liu, Yuhan, Zhang, Xinyu, Schramm, Liam, Boularias, Abdeslam
Solving storage problem: where objects must be accurately placed into containers with precise orientations and positions, presents a distinct challenge that extends beyond traditional rearrangement tasks. These challenges are primarily due to the nee
Externí odkaz:
http://arxiv.org/abs/2409.00499
Autor:
Schramm, Liam, Boularias, Abdeslam
Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning and imitation learning. We explore using diffusion as a model class for
Externí odkaz:
http://arxiv.org/abs/2407.12163
Autor:
Schramm, Liam, Boularias, Abdeslam
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 algorithms like Rapidly-Exploring Random Trees. To address these limitation
Externí odkaz:
http://arxiv.org/abs/2407.05511
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 one goal as successful trajectories for another. This allows for both
Externí odkaz:
http://arxiv.org/abs/2207.01115
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 practitioners to reduce data collection with physical robots, which can be
Externí odkaz:
http://arxiv.org/abs/2005.10418