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Found 6 papers, 2 papers with code
AMPLIFY: Actionless Motion Priors for Robot Learning from Videos
Our modular approach separates visual motion prediction from action inference, decoupling the challenges of learning what motion defines a task from how robots can perform it.
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning
Imitation Learning can train robots to perform complex and diverse manipulation tasks, but learned policies are brittle with observations outside of the training distribution.
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations
Providing densely shaped reward functions for RL algorithms is often exceedingly challenging, motivating the development of RL algorithms that can learn from easier-to-specify sparse reward functions.
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation learning: is it possible to control the timing and length of interventions to both facilitate learning and limit burden on the human supervisor?
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
We then present a new algorithm, Latent Space Safe Sets (LS3), which uses this representation for long-horizon tasks with sparse rewards.
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning.
