Addressing Ambiguity in Imitation Learning through Product of Experts based Negative Feedback
Addressing Ambiguity in Imitation Learning through Product of Experts based Negative Feedback explores A novel imitation learning system that leverages suboptimal human demonstrations and learns from its own failures to significantly improve robot task success rates.. Commercial viability score: 7/10 in Robotics Imitation Learning.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisLearning Human-Like Badminton Skills for Humanoid RobotsViability: 6.0
- Builds On ThisMoE-ACT: Improving Surgical Imitation Learning Policies through Supervised Mixture-of-ExpertsViability: 6.0
- Builds On ThisEmboCoach-Bench: Benchmarking AI Agents on Developing Embodied RobotsViability: 6.0
- Builds On ThisATG-MoE: Autoregressive trajectory generation with mixture-of-experts for assembly skill learningViability: 3.0
- Prior WorkEasy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via Assistant Experts