Annie Chen
@_anniechen_
PhD student @StanfordAILab. Prev: research @GoogleDeepMind, Stanford BS/MS
How should an RL agent leverage expert data to improve sample efficiency? Imitation losses can overly constrain an RL policy. In RL via Implicit Imitation Guidance, we show how to use expert data to guide more efficient *exploration*, avoiding pitfalls of imitation-augmented RL
🚨 We’re thrilled to announce our ICCV 2025 Workshop: MMRAgI – Multi-Modal Reasoning for Agentic Intelligence! 🚨 🌐 Homepage: agent-intelligence.github.io/agent-intellig… 📥 Submit: openreview.net/group?id=thecv… 🗓️ Submission Deadline (Proceeding Track): June 24th 2025 23:59 AoE 🗓️ Submission Deadline…
How can robots problem solve in novel environments? We combine high-level reasoning with VLMs with low-level controllers to allow test-time problem solving. Paper & code: anniesch.github.io/vlm-pc/
How can robots autonomously handle ambiguous situations that require commonsense reasoning? *VLM-PC* provides adaptive high-level planning, so robots can get unstuck by exploring multiple strategies. Paper: anniesch.github.io/vlm-pc/
Data curation is crucial for post-training recipes. But how do we curate? Curation is usually manual & tedious. And, it's hard to tell if a strategy in the data will be reliable! We introduce an automatic way to curate, informed by the robot's policy learning.
Not all human-collected demos are created equal: ✔️ All are successful ❌ But some strategies are unreliable or brittle This can hurt final performance. Demo-SCORE self-curates reliable training data using online experience. Paper and videos: anniesch.github.io/demo-score/