TMRL bridges behavioral cloning pretraining and RL finetuning via diffusion noise and timestep modulation to enable controlled exploration, improving sample efficiency and enabling real-world robot training in under one hour.
Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Global-MPPI integrates kernel SOS global search with MPPI local refinement and graduated non-convexity smoothing to achieve faster convergence and lower costs on high-dimensional contact-rich manipulation tasks.
citing papers explorer
-
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
TMRL bridges behavioral cloning pretraining and RL finetuning via diffusion noise and timestep modulation to enable controlled exploration, improving sample efficiency and enabling real-world robot training in under one hour.
-
Global Sampling-Based Trajectory Optimization for Contact-Rich Manipulation via KernelSOS
Global-MPPI integrates kernel SOS global search with MPPI local refinement and graduated non-convexity smoothing to achieve faster convergence and lower costs on high-dimensional contact-rich manipulation tasks.