Unverified50% confidenceFactExact time
Ablation experiments show that inserting a learned motion planner between the VLM and the robot raises the RLBench success rate to about 90%.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/19/2026
First Seen
Valid until: 1/17/2027
Sources
Related Entities
Related Claims
UnverifiedBreakthrough directions explored by various laboratories in the post-training stage include RLHF, chain-of-thought distillation, and enhanced tool-calling capabilities.65% similarUnverifiedResearch from institutions such as ETH Zurich and the University of California, Berkeley shows that RL policies trained in simulation can enable quadruped robots to achieve near-animal-level gait adaptability on rough terrain without requiring precise dynamic modeling65% similarUnverifiedThe LoopWAM paper proposes a closed-loop world action model that can learn from rollouts of successful, suboptimal, and failed deployments, and after three rounds of deployment training on Robot Challenge Table 30-VR, it raised the success rate from 23.67% to 43.33%, an increase of 19.7 percentage points.64% similarUnverifiedOpenAI trained a robotic arm using purely simulated data and achieved a success rate of over 90% in the real world63% similarUnverifiedThe essence of choosing a simulation platform is a trade-off between RL training efficiency and physical fidelity63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1065628API
curl https://kongchang.com/api/v1/knowledge/claims/1065628MCP
get_claim(id=1065628)