A blackboard multi-agent system parses STEP B-Reps, injects GraphSAGE feature labels, and uses Claude plus GPT-4o to reorient and modify FDM parts for overhang-free printability.
One act play: Single demonstration behavior cloning with action chunking transformers
6 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
6
Pith papers citing it
2
external citations · external index
representative citing papers
Q-chunking improves offline-to-online RL sample efficiency on long-horizon sparse-reward manipulation tasks by applying action chunking to TD learning.
Real-time chunking (RTC) allows diffusion- and flow-based action chunking policies to execute smoothly and asynchronously, maintaining high success rates on dynamic tasks even with significant inference latency.
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.
VR-DAgger is a VR-centered human-in-the-loop framework that applies MC dropout uncertainty to select and correct failure segments in diffusion policy rollouts, yielding up to 23 percentage point gains over behavioral cloning and 40% lower per-sample collection time on three dexterous tasks.
citing papers explorer
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AgentsCAD: Automated Design for Manufacturing of FDM Parts via Multi-Agent LLM Reasoning and Geometric Feature Recognition
A blackboard multi-agent system parses STEP B-Reps, injects GraphSAGE feature labels, and uses Claude plus GPT-4o to reorient and modify FDM parts for overhang-free printability.
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Reinforcement Learning with Action Chunking
Q-chunking improves offline-to-online RL sample efficiency on long-horizon sparse-reward manipulation tasks by applying action chunking to TD learning.
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Real-Time Execution of Action Chunking Flow Policies
Real-time chunking (RTC) allows diffusion- and flow-based action chunking policies to execute smoothly and asynchronously, maintaining high success rates on dynamic tasks even with significant inference latency.
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Robots Need More than VLA and World Models
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
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One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors
A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.
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VR-DAgger: Immersive VR for Dexterous Data Collection and Uncertainty-Guided On-Policy Correction
VR-DAgger is a VR-centered human-in-the-loop framework that applies MC dropout uncertainty to select and correct failure segments in diffusion policy rollouts, yielding up to 23 percentage point gains over behavioral cloning and 40% lower per-sample collection time on three dexterous tasks.