FIERCE: From Generalist Robot Policies to Fast Specialists via Progress-Failure Feedback
cs.RO
Submitted: 2026-09-16
Updated: 2026-09-16
Code: https://github.com/ar-mine/FIERCE
Terminology
Sources
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- World Action Models are Zero-shot Policies
- Real-Time Execution of Action Chunking Flow Policies
- Refined Policy Distillation: From VLA Generalists to RL Experts
- Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
- Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
- Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- RL Token: Bootstrapping Online RL with Vision-Language-Action Models
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- RoboReward: General-Purpose Vision-Language Reward Models for Robotics
- A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- ViVa: A Video-Generative Value Model for Robot Reinforcement Learning
- BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
- World Model Failure Classification and Anomaly Detection for Autonomous Inspection
- Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation
- Cosmos 3: Omnimodal World Models for Physical AI
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- LoRA: Low-Rank Adaptation of Large Language Models
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