← Search

Akshay L. Chandra

2 accepted papers

2025

DiWA: Diffusion Policy Adaptation with World Models

CoRL 2025poster

Fine-tuning diffusion policies with reinforcement learning (RL) presents significant challenges. The long denoising sequence for each action prediction impedes effective reward propagation. Additionally, standard RL methods require millions of physical interaction steps, making fine-tuning even more…

Cited by 0SourceScholar
2025

LUMOS: Language-Conditioned Imitation Learning with World Models

ICRA 2025

We introduce LUMOS, a language-conditioned multi-task imitation learning framework for robotics. LUMOS learns skills by practicing them over many long-horizon rollouts in the latent space of a learned world model and transfers these skills zero-shot to a real robot. By learning on-policy in the late

Cited by 13SourceScholar