Hermite Curves as Trajectory Priors for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2608.01265v2
• Date: Sun, 09 Aug 2026 11:46:24 GMT
• Title: Hermite Curves as Trajectory Priors for Vision-Language-Action Models
• Abstract summary: We introduce Hermite trajectory priors, parameterizing the chunk trajectory as a piecewise cubic Hermite curve defined by endpoint positions and velocities.<n>We instantiate this fixed operator across discrete autoregressive and continuous generative paradigms.<n>Across simulation benchmarks and real-robot platforms, Hermite Regularization achieves superior performance.
• Score: 41.51953284790607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite recent progress in Vision-Language-Action (VLA) models for robotic manipulation, the action chunk remains a weakly structured interface. Existing work typically flatten each chunk into per-timestep controls, relying on implicit data learning that manifests as jagged motion and boundary discontinuities during physical execution. To address these limitations, we introduce Hermite trajectory priors, parameterizing the chunk trajectory as a piecewise cubic Hermite curve defined by endpoint positions and velocities to explicitly enforce smoothness and continuity. We instantiate this fixed operator across discrete autoregressive and continuous generative paradigms via three variants: (1) Hermite Tokens, which predict quantized boundary variables autoregressively; (2) Hermite Scaffold, which decomposes clean actions into a base scaffold and residuals; and (3) Hermite Regularization, which applies the prior strictly as an auxiliary training objective. Across simulation benchmarks and real-robot platforms, Hermite Regularization achieves superior performance among these three variants, improving π0.5 baseline success rates from 95.9% to 98.7% on LIBERO, 85.7% to 90.9% on LIBERO-plus, and 63.4% to 90.0% across four real-robot tasks without additional inference overhead. Trajectory analyses reveal that explicitly structuring trajectory priors serves most effectively as a learning inductive bias rather than a runtime constraint.

Related papers

• PAVE: Predictive Alignment and Value-Guided Evolution for World-Action Policies [17.87859739145508]
  Direct vision-language-action policies generate continuous robot actions efficiently, but standard behavior cloning leaves two complementary gaps.<n>We introduce method, a direct world-action policy that combines outcome-agnostic predictive learning with outcome-aware policy improvement.
  arXiv  Detail & Related papers  (2026-08-31T07:36:11Z)
• LM-X: Explainable Action Modeling with Progress, Event, and Uncertainty Prediction for Generalist Robot Manipulation [28.431160101245837]
  Generalist vision--language--action (VLA) policies learn long-horizon behavior mainly through short-horizon action prediction.<n>We introduce LM-X, which organizes prediction across task, event, and motor scales without claiming anatomical correspondence.
  arXiv  Detail & Related papers  (2026-08-26T13:05:29Z)
• RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance [84.26294415726723]
  General-purpose reward models are increasingly the bottleneck for scaling robot learning.<n>We introduce RynnValue, an open-source value foundation model for robotic manipulation.<n>RynnValue scales to over 7,000 hours and roughly 3M instruction-conditioned clips without preference or progress annotations.
  arXiv  Detail & Related papers  (2026-08-10T17:09:37Z)
• TRACT: Temporally Routed Action Chunks with Chronological Phase Authority for Contact-Rich Manipulation [33.980402689056895]
  Action chunking shortens the effective decision horizon of robot imitation learning.<n>We present TRACT, which factorizes phase-structured action chunking into an accepted current phase and a single CURRENT variants-to- boundary inside the future horizon.
  arXiv  Detail & Related papers  (2026-07-31T10:59:14Z)
• SketchMamba: A Lightweight State-Space Model for Joint Progressive Sketch Classification and Stroke Auto-Completion [0.0]
  Existing vector-sketch models treat recognition and generation as separate tasks.<n>We present SketchMamba, a single causal sequence model that continuously classifies a sketch from any partial prefix while simultaneously generating its continuation.
  arXiv  Detail & Related papers  (2026-07-26T10:05:23Z)
• Discrete Action Space as a Prerequisite for GRPO Convergence in Small-Model Continuous Control [0.0]
  Group Relative Policy Optimization can fine-tune small language models for simulated quadrotor continuous-control tasks.<n>In our benchmark, vanilla GRPO fine-tuning of Qwen-0.5B for 25 Hz quadrotor velocity control collapses to the trivial zero action.<n>A high-fidelity simulation using Crazyflie 2.1 dynamics surfaces a hover-region training-distribution gap.
  arXiv  Detail & Related papers  (2026-07-07T17:14:19Z)
• PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models [50.232333672172395]
  Vision-Language-Action (VLA) models provide a unified paradigm for robotic manipulation, yet their real-world deployment is often bottlenecked by execution efficiency.<n>We propose textbfPolicyTrim, a reinforcement learning-based post-training framework that extends the reliable action chunk length and reduces redundant physical steps.<n>Our framework delivers up to a 5.83$times$ end-to-end deployment speedup without compromising task success rates.
  arXiv  Detail & Related papers  (2026-06-21T14:54:07Z)
• ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation [57.07494675832939]
  Most existing vision-language-action (VLA) models for robotic manipulation lack progress awareness.<n>In this work, we investigate the estimation and integration of task progress, proposing a novel model named textbf vla<n>Experiments on the CALVIN and LIBERO benchmarks, alongside real-world robot deployment, consistently demonstrate substantial improvements in success rates.
  arXiv  Detail & Related papers  (2026-03-29T12:38:11Z)
• Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation [95.89924101984566]
  We introduce OptimusVLA, a dual-memory VLA framework with Global Prior Memory (GPM) and Local Consistency Memory (LCM)<n>GPM replaces Gaussian noise with task-level priors retrieved from semantically similar trajectories.<n>LCM injects a learned consistency constraint that enforces temporal coherence and smoothness of trajectory.
  arXiv  Detail & Related papers  (2026-02-22T15:39:34Z)
• Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching [25.295588774254952]
  We reformulate inference acceleration as a learnable policy optimization problem.<n>We propose a novel framework that integrates a task-aware decision-making process directly into the Vision-Language-Action model.<n>Our method achieves a 1.76x wall-clock inference speedup while simultaneously improving the average success rate.
  arXiv  Detail & Related papers  (2026-01-31T12:12:51Z)
• Unsupervised Cycle Detection in Agentic Applications [7.001329254828447]
  Agentic applications powered by Large Language Models exhibit non-deterministic behaviors that can form hidden execution cycles.<n>Traditional observability platforms fail to detect these costly inefficiencies.<n>We present an unsupervised cycle detection framework that combines structural and semantic analysis.
  arXiv  Detail & Related papers  (2025-10-31T13:27:53Z)
• Transition Models: Rethinking the Generative Learning Objective [68.16330673177207]
  We introduce a continuous-time dynamics equation that analytically defines state transitions across any finite time interval.<n>This leads to a novel generative paradigm, Transition Models (TiM), which adapt to arbitrary-step transitions.<n>TiM achieves state-of-the-art performance, surpassing leading models such as SD3.5 (8B parameters) and FLUX.1 (12B parameters) across all evaluated step counts.
  arXiv  Detail & Related papers  (2025-09-04T17:05:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.