arXiv (All)
open access
Steering Multirobot Behavior via Closed-Loop Affine Activation Editing
Real-world robots need to adapt their behavior beyond the envelope of their pre-trained policy. Policy finetuning or retraining are options, but they risk catastrophic forgetting, degrading the pretrained policy's base performance. To combat this, we introduce CLAE: Closed-Loop Affine Activation Edi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SmoothRL: Online Reinforcement Learning During Asynchronous Execution#798397
- SmoothRL: Online Reinforcement Learning During Asynchronous Execution#796984
- Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies#1143138
- Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots#158639
- LYRIC: Language-Driven Physics-Based Character Control for Contact-Rich Whole-Body Object Interaction#1014276
Record · ID 1051472
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive