TaRL: Learning General and Physical Rewards from Tactile Demonstrations

arXiv:2609.36785 · cs.RO · Submitted 2026-09-29 · Read on arXiv

cs.RO

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/TheRobotStudio/SO-ARM100

Project page: https://embodiedai-ntu.github.io/tarl

Terminology

• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• Reward Function — 12× in this paper · explained in 2 episodes
• Generalization — 10× in this paper · explained in 13 episodes
• Conference on Robot Learning — 4× in this paper
• Actor — 3× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 3× in this paper
• International Conference on Machine Learning — 3× in this paper
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• International Conference on Robotics and Automation — 2× in this paper
• Reward Shaping — 2× in this paper · explained in 2 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• RL with Implicit Q-Learning — 1× in this paper
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• Concrete Problems in AI Safety
• A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
• DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos
• Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation
• Proximal Policy Optimization Algorithms
• Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning

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