ReCoVLA improves VLA policy reliability by using a VLM as a semantic reward selector to train residual recovery policies in simulation, raising average success from 36.7% to 66.7% in sim and achieving 61.7% in zero-shot sim-to-real physical tests.
Racer: Rich language-guided failure recovery policies for imitation learning
6 Pith papers cite this work. Polarity classification is still indexing.
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VoLoAgent uses a VLM to steer heterogeneous robot capabilities as interruptible tools for long-horizon manipulation and introduces the RoboVoLo benchmark, claiming substantial outperformance over single VLA/VLM or tool-based systems with real-robot validation.
Instrumented objects boost diffusion policy success in robotic hanger insertion by 14-25 percentage points over vision-only baselines, and augmenting datasets with instrumented expert rollouts lets a vision-only student match the instrumented expert.
Sentinel-VLA adds metacognitive status monitoring to VLA models for on-demand reasoning and error recovery, reporting over 30% higher real-world task success than prior SOTA.
A hierarchical VLA architecture lets robots follow complex instructions and situated feedback by separating high-level reasoning from low-level control.
A passively compliant soft wrist structures insertion as sequential contact formations and uses a VLM to recover from failures, reaching 83% success in simulation across randomized grasp, pose, friction, and shape variations with real-robot validation.
citing papers explorer
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ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
ReCoVLA improves VLA policy reliability by using a VLM as a semantic reward selector to train residual recovery policies in simulation, raising average success from 36.7% to 66.7% in sim and achieving 61.7% in zero-shot sim-to-real physical tests.
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VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation
VoLoAgent uses a VLM to steer heterogeneous robot capabilities as interruptible tools for long-horizon manipulation and introduces the RoboVoLo benchmark, claiming substantial outperformance over single VLA/VLM or tool-based systems with real-robot validation.
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Instrumentation for Imitation Learning: Enhancing Training Datasets for Clothes Hanger Insertion
Instrumented objects boost diffusion policy success in robotic hanger insertion by 14-25 percentage points over vision-only baselines, and augmenting datasets with instrumented expert rollouts lets a vision-only student match the instrumented expert.
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Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
Sentinel-VLA adds metacognitive status monitoring to VLA models for on-demand reasoning and error recovery, reporting over 30% higher real-world task success than prior SOTA.
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Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
A hierarchical VLA architecture lets robots follow complex instructions and situated feedback by separating high-level reasoning from low-level control.
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Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery
A passively compliant soft wrist structures insertion as sequential contact formations and uses a VLM to recover from failures, reaching 83% success in simulation across randomized grasp, pose, friction, and shape variations with real-robot validation.