Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures

arXiv:2606.18328 · cs.RO · Submitted 2026-06-16 · Read on arXiv

cs.RO

Submitted: 2026-06-16

Updated: 2026-10-08

Project page: https://jaraxxus-me.github.io/ReSYNC

Terminology

• Abstraction — 26× in this paper · explained in 1 episode
• Reinforcement Learning — 16× in this paper · explained in 4 episodes
• Generalization — 9× in this paper · explained in 13 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Hyperparameters — 6× in this paper · explained in 2 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Sample Efficiency — 3× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 6 episodes
• Deep Skill Graphs — 2× in this paper
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• International Conference on Robotics and Automation — 2× in this paper
• Recovery Chaining — 2× in this paper
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Temporal Abstraction — 2× in this paper · explained in 2 episodes
• Actor-Critic Architecture — 1× in this paper · explained in 1 episode
• Actor-Critic Framework — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Planning — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Hierarchical Reinforcement Learning (HRL) — 1× in this paper · explained in 2 episodes
• Proceedings of International Conference on Machine Learning — 1× in this paper
• Proceedings of the Conference on Robot Learning — 1× in this paper
• Reward Function — 1× in this paper · explained in 2 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Test-Time Recovery Learning — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
• From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
• SLAP: Shortcut Learning for Abstract Planning
• Symskill: Symbol and Skill Co-Invention for Data-Efficient and Reactive Long-Horizon Manipulation
• Relational inductive biases, deep learning, and graph networks
• Unifying Deep Predicate Invention with Pre-trained Foundation Models
• Semi-parametric Topological Memory for Navigation
• Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots
• SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
• Proximal Policy Optimization Algorithms
• KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
• Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots
• Solving Rubik's Cube with a Robot Hand
• SAM 3: Segment Anything with Concepts
• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding
• Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs
• From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
• AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
• LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning

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• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• ARC: A Reasoning Recipe for Robot Foundation Models
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies