Learning Multi-Stage Tasks with One Demonstration via Self-Replay
In this work, we introduce a novel method to learn everyday-like multi-stage tasks from a single human demonstration, without requiring any prior object knowledge. Inspired by the recent Coarse-to-Fine Imitation Learning method, we model imitation learning as a learned object reaching phase followed by an open-loop replay of the demonstrator's actions. We build upon this for multi-stage tasks where, following the human demonstration, the robot can autonomously collect image data for the entire multi-stage task, by reaching the next object in the sequence and then replaying the demonstration, and then repeating in a loop for all stages of the task. We evaluate with real-world experiments on a set of everyday-like multi-stage tasks, which we show that our method can solve from a single demonstration. Videos and supplementary material can be found at https://www.robot-learning.uk/self-replay.
Code (0)
등록된 구현이 없습니다.
Tasks
Imitation LearningObjectSimilar Papers 제목 키워드 기반
Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning
We investigate the effect of using human demonstration data in the replay buffer for Deep Reinforcement Learning. We use a policy gradient method with a modified experience replay buffer where a human demonstration exper…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
Supervised fine-tuning (SFT) has become the de facto post-training strategy for large vision-language-action (VLA) models, but its reliance on costly human demonstrations limits scalability and generalization. We propose…
Reinforcement LearningSTIR$^2$: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks
In the search for more sample-efficient reinforcement-learning (RL) algorithms, a promising direction is to leverage as much external off-policy data as possible. For instance, expert demonstrations. In the past, multipl…
Autonomous DrivingDecision MakingImitation LearningReinforcement Learning (RL)Learning Memory-Dependent Continuous Control from Demonstrations
Efficient exploration has presented a long-standing challenge in reinforcement learning, especially when rewards are sparse. A developmental system can overcome this difficulty by learning from both demonstrations and se…
continuous-controlContinuous ControlDecision MakingEfficient Exploration+3One-Shot Dual-Arm Imitation Learning
We introduce One-Shot Dual-Arm Imitation Learning (ODIL), which enables dual-arm robots to learn precise and coordinated everyday tasks from just a single demonstration of the task. ODIL uses a new three-stage visual ser…
Imitation Learning