RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes

arXiv:2610.11480 · cs.RO, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Ablation — 15× in this paper · explained in 1 episode
• Benchmark — 15× in this paper · explained in 2 episodes
• Q-learning — 12× in this paper · explained in 3 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• Execution-Aware Learning — 6× in this paper
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• In Conference on Robot Learning — 4× in this paper
• Trace — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Affordance — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• IEEE International Conference on Robotics and Automation — 2× in this paper
• In International Conference on Machine Learning — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Selection Bias — 2× in this paper · explained in 4 episodes
• State-Locked Counterfactual Branching — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Modular Components — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structured Prompting — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents
• What Matters in Orchestrating Robot Policies: A Systematic Study of Hierarchical VLA Agents
• RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning
• What Frozen VLAs Already Know About Success: A Probing Study of Value-Like Structure in Foundation Robot Policies
• InstructSAM: Segment Any Instance with Any Instructions
• cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
• robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
• Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models
• Playful Agentic Robot Learning
• ASPIRE: Agentic /Skills Discovery for Robotics
• Addressing the Orchestration Gap in Generalist Robots via Physical Agency
• VLS: Steering Pretrained Robot Policies via Vision-Language Models
• Act-Observe-Rewrite: Multimodal Coding Agents as In-Context Policy Learners for Robot Manipulation
• RHO: Your Coding Agent is Secretly a Roboticist
• GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
• PhysCaP: Grounding Code-as-Policy Agent with Physics-Informed Exploration

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