Unverified50% confidenceFactExact time
PaceVLA was evaluated on three multi-task simulation benchmarks and six real-world tasks, covering multiple robot embodiments, including long-horizon tasks and dexterous manipulation.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/20/2026
First Seen
Valid until: 1/18/2027
Sources
Related Entities
Related Claims
Unverified该项目中roboreg负责机器人位姿估计，OpTaS负责实时求解带约束的导纳任务73% similarUnverifiedPaceVLA's action expert is a single shared backbone network dedicated to action generation that receives visual and language representations and outputs latent representations of action sequences, rather than multiple expert networks.68% similarUnverifiedThe core research methodology was to have three agents each execute on a unified task set, recording the type, frequency, and sequence of each tool call to form comparable behavioral profiles.67% similarUnverifiedThe research team deployed the policy on a real Stretch 3 robot, achieving a 63.9% success rate on a subset of 12 tasks, compared to a simulation success rate of 66.7% on those same tasks.66% similarUnverifiedAnyGoal constructs "goal-shift" task variants built on widely used world model environments.66% similar
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