QuadKAN: KAN-Enhanced Quadruped Motion Control via End-to-End Reinforcement Learning

• URL: http://arxiv.org/abs/2508.19153v2
• Date: Sun, 07 Sep 2025 01:11:35 GMT
• Title: QuadKAN: KAN-Enhanced Quadruped Motion Control via End-to-End Reinforcement Learning
• Abstract summary: We address vision-guided quadruped motion control with reinforcement learning (RL)<n>We propose QuadKAN, a cross-modal policy instantiated with Kolmogorov-Arnold Networks (KANs)<n>We show that QuadKAN achieves consistently higher returns, greater distances, and fewer collisions than state-of-the-art (SOTA) baselines.
• Score: 2.26464274357758
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We address vision-guided quadruped motion control with reinforcement learning (RL) and highlight the necessity of combining proprioception with vision for robust control. We propose QuadKAN, a spline-parameterized cross-modal policy instantiated with Kolmogorov-Arnold Networks (KANs). The framework incorporates a spline encoder for proprioception and a spline fusion head for proprioception-vision inputs. This structured function class aligns the state-to-action mapping with the piecewise-smooth nature of gait, improving sample efficiency, reducing action jitter and energy consumption, and providing interpretable posture-action sensitivities. We adopt Multi-Modal Delay Randomization (MMDR) and perform end-to-end training with Proximal Policy Optimization (PPO). Evaluations across diverse terrains, including both even and uneven surfaces and scenarios with static or dynamic obstacles, demonstrate that QuadKAN achieves consistently higher returns, greater distances, and fewer collisions than state-of-the-art (SOTA) baselines. These results show that spline-parameterized policies offer a simple, effective, and interpretable alternative for robust vision-guided locomotion. A repository will be made available upon acceptance.

Related papers

• Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach [60.596944437968524]
  PartialVisGraph is a novel framework for robust skeleton-based action recognition under constrained field-of-view.<n>It consistently achieves state-of-the-art accuracy under partial visibility, with gains of up to 68.8% on subsets with severe FoV restrictions.<n>Our approach offers a principled and practical pathway toward deployable skeleton-based action understanding in unconstrained environments.
  arXiv  Detail & Related papers  (2026-07-01T10:03:11Z)
• Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach [31.34066005342801]
  This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS)<n>Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link from the Base Station (BS) to the user to enable dynamic uplink power control.<n>To navigate the discrete and decentralized nature of this active sensing problem, we propose a novel Dual-Agent (DA) deep learning framework.
  arXiv  Detail & Related papers  (2026-06-30T07:43:28Z)
• Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought [73.39221516441624]
  Multimodal Chain-of-Thought (CoT) reasoning requires large vision-language models to construct reasoning trajectories.<n>Existing Reinforcement Learning with Verifiable Rewards (RLVR) methods treat CoT uniformly without distinguishing varying degrees of visual grounding.<n>We propose Perception-Exploration Policy Optimization (PEPO), which derives a perception prior to hidden state similarity and integrates it with token entropy.
  arXiv  Detail & Related papers  (2026-03-24T06:38:00Z)
• Beyond Imitation: Reinforcement Learning Fine-Tuning for Adaptive Diffusion Navigation Policies [31.52910494173408]
  Diffusion-based robot navigation policies can generate multi-modal trajectories directly from the robot's visual observations.<n>We propose a reinforcement learning framework tailored for diffusion-based navigation.<n>Our approach improves the Success Rate from 52.0% to 58.7% and SPL from 0.49 to 0.54 on unseen scenes, while reducing collision frequency.
  arXiv  Detail & Related papers  (2026-03-13T10:14:32Z)
• ADM-DP: Adaptive Dynamic Modality Diffusion Policy through Vision-Tactile-Graph Fusion for Multi-Agent Manipulation [6.476276375428053]
  We propose a framework that integrates vision, tactile, and graph-based (multi-agent pose) modalities for coordinated control.<n>Across seven multi-agent tasks, ADM-DP achieves 12-25% performance gains over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-25T06:35:19Z)
• OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [63.388513841293616]
  Existing forgery detection methods fail to handle the interleaved text, images, and videos prevalent in real-world misinformation.<n>To bridge this gap, this paper targets to develop a unified framework for omnibus vision-language forgery detection and grounding.<n>We propose textbf OmniVL-Guard, a balanced reinforcement learning framework for omnibus vision-language forgery detection and grounding.
  arXiv  Detail & Related papers  (2026-02-11T09:41:36Z)
• Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics [81.80010043113445]
  Local weight fine-tuning, LoRA-based adaptation, and activation-based interventions are studied in isolation.<n>We present a unified view that frames these interventions as dynamic weight updates induced by a control signal.<n>Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility.
  arXiv  Detail & Related papers  (2026-02-02T17:04:36Z)
• RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering [62.63376387138257]
  We propose a plug-and-play intervention framework that adaptively steers large language models (LLMs) reasoning in activation space.<n>RISER constructs a library of reusable reasoning vectors and employs a lightweight Router to dynamically compose them for each input.<n>The Router is optimized via reinforcement learning under task-level rewards, activating latent cognitive primitives in an emergent and compositional manner.
  arXiv  Detail & Related papers  (2026-01-14T08:04:33Z)
• Equivariant Goal Conditioned Contrastive Reinforcement Learning [5.019456977535218]
  Contrastive Reinforcement Learning (CRL) provides a promising framework for extracting useful structured representations from unlabeled interactions.<n>We propose Equivariant CRL, which further structures the latent space using equivariant constraints.<n>Our approach consistently outperforms strong baselines across a range of simulated tasks in both state-based and image-based settings.
  arXiv  Detail & Related papers  (2025-07-22T01:13:45Z)
• SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuning [17.104905795008555]
  We propose a framework named Selective Cross-modal Prompt Tuning (SCING) that enhances cross-modal alignment and robustness against real-worlds.<n>Our framework eliminates heavy adapters while maintaining efficient inference, achieving an optimal trade-off between performance and computational overhead.
  arXiv  Detail & Related papers  (2025-07-01T07:21:31Z)
• Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts [79.18608192761512]
  Self-Explainable Models (SEMs) rely on Prototypical Concept Learning (PCL) to enable their visual recognition processes more interpretable.<n>We propose a Few-Shot Prototypical Concept Classification framework that mitigates two key challenges under low-data regimes: parametric imbalance and representation misalignment.<n>Our approach consistently outperforms existing SEMs by a notable margin, with 4.2%-8.7% relative gains in 5-way 5-shot classification.
  arXiv  Detail & Related papers  (2025-06-05T06:39:43Z)
• ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding [71.654781631463]
  ReAgent-V is a novel agentic video understanding framework.<n>It integrates efficient frame selection with real-time reward generation during inference.<n>Extensive experiments on 12 datasets demonstrate significant gains in generalization and reasoning.
  arXiv  Detail & Related papers  (2025-06-02T04:23:21Z)
• MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion [1.479858319622657]
  Quadrupedal robots are increasingly deployed for load-carrying tasks across diverse terrains.<n>We propose an Adaptive Reinforcement Learning framework that enables quadrupedal robots to adapt to both varying payloads and diverse terrains.<n>We validate the proposed approach through large-scale simulation experiments in Isaac Gym and real-world hardware deployment on a Unitree Go1 quadruped.
  arXiv  Detail & Related papers  (2025-05-01T12:41:35Z)
• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech.<n>The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture.<n>To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition [52.89441679581216]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise.<n>We present an innovative video decomposition strategy that incorporates view-independent and view-dependent components.<n>Our framework consistently outperforms existing methods, establishing a new SOTA performance.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds [21.6511040107249]
  We propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. We predict scene motion via feature-level consistency between pillars in consecutive frames, which can eliminate the effect caused by noise points and view-changing point clouds in dynamic scenes.
  arXiv  Detail & Related papers  (2023-04-25T05:46:24Z)
• RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control [6.669503016190925]
  We present a unified model-based and data-driven approach for quadrupedal planning and control. We map sensory information and desired base velocity commands into footstep plans using a reinforcement learning policy. We train and evaluate our framework on a complex quadrupedal system, ANYmal B, and demonstrate transferability to a larger and heavier robot, ANYmal C, without requiring retraining.
  arXiv  Detail & Related papers  (2020-12-05T18:30:23Z)
• First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion [73.37945453998134]
  Traditional approaches to quadruped control employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
  arXiv  Detail & Related papers  (2020-07-03T07:04:18Z)

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.