arXiv AI
Jun 17
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
arXiv:2606. 18247v1 Announce Type: cross Abstract: Robots deployed in the real world should learn from their experience and improve over time.
Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling
The paper introduces Quality-Controllable Policy (QCP), a robot policy that allows real‑time adjustment of quality attributes such as speed and smoothness during task execution. QCP employs an iterative hindsight relabeling loop, where online rollouts are collected under target quality labels, successful trajectories are added to the dataset, and trajectories are relabeled based on measured quality before fine‑tuning the policy. Experiments on Robomimic, RoboEval, and real‑world manipulation tasks show that QCP improves both task execution quality and success rates, enabling higher throughput in conveyor operations and better stability when carrying fragile objects.
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arXiv AI
Jun 2
SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning
arXiv:2512. 00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human operation.
arXiv AI
Aug 11
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
arXiv:2608. 09138v1 Announce Type: cross Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds.
Hugging Face Trending Papers
Aug 10
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learning policies are inherently limited by hardware constraints and the speed of the operator during data collection.
arXiv AI
Oct 7
Guided Action Flow: Value-Guided Sampling for Frozen Vision-Language-Action Policies
arXiv:2607.02092v4 Announce Type: replace-cross Abstract: Reinforcement learning can improve vision-language-action (VLA) policies beyond supervised fine-tuning, although this typically involves furt...
arXiv AI
Jun 9
Benchmarking Vision-Language-Action Models on SO-101: Failure and Recovery Analysis
arXiv:2606. 08881v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored.
arXiv Machine Learning
Jun 11
OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.
arXiv AI
Aug 10
AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies
arXiv:2608. 07065v1 Announce Type: cross Abstract: Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency by predicting short action sequences rather than single-step commands.
arXiv Machine Learning
Aug 24
Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning
arXiv:2608.21204v1 Announce Type: cross Abstract: Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a poli...
arXiv AI
Oct 26
Retiming Robot Demonstrations with a Learned Speed Prior
The paper introduces a learned speed prior that predicts a distribution over execution speed for robot demonstrations, allowing consistent retiming across tasks. By applying this prior to both robot teleoperation and handheld demonstrations, the authors retime demonstrations to a handheld pace before fine‑tuning a vision‑language‑action policy. Experiments on 120 RoboLab tasks and a real robot show that policies trained with the learned clocks achieve higher success rates and smoother motion, outperforming policies fine‑tuned on original or uniformly accelerated timings.
arXiv AI
Oct 23
PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models
PaceVLA introduces adaptive temporal action quantization for Vision‑Language‑Action models, enabling faster robot execution by aggregating low‑level actions into coarser steps when appropriate. The framework combines mode‑specific action decoders with a quantizability‑guided mode selection and a context‑conditioned router to decide execution granularity at each inference step. Experiments on simulation benchmarks and real‑world tasks demonstrate that PaceVLA improves execution efficiency while preserving task success rates across diverse embodiments.
Hugging Face Trending Papers
Oct 19
PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models
Fast and reliable manipulation is crucial for practical robot deployment. Although Vision-Language-Action (VLA) models have recently shown remarkable progress as generalist robot policies, their execu...