L-SDPPO: Policy Optimization of Spiking Diffusion Policy for Intra-vehicular Robotic Manipulation

• URL: http://arxiv.org/abs/2606.06049v1
• Date: Thu, 04 Jun 2026 11:45:37 GMT
• Title: L-SDPPO: Policy Optimization of Spiking Diffusion Policy for Intra-vehicular Robotic Manipulation
• Abstract summary: We propose a low-energy intra-vehicular robotic manipulation framework, L-SDP, in which the Spiking Diffusion Policy (SDP) is optimized with a reinforcement learning (RL) algorithm.<n>We show that our method consistently achieves higher success rates and lower energy consumption, compared to the state-of-the-art robotic manipulation methods.
• Score: 8.231045397003117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intra-vehicular robots in spacecraft help reduce astronaut workload and improve mission efficiency. Recent research focuses on using deep learning methods to achieve the acute control required for operations in these complex environments. However, objects exhibit unpredictable, unconstrained drift without gravitational damping. These factors demand robustness against complex multimodal action distributions. Diffusion policies (DP) can model these complex actions, but their iterative sampling process consumes too much energy for the limited power budgets of spacecraft. We therefore propose a low-energy intra-vehicular robotic manipulation framework, L-SDPPO, in which the Spiking Diffusion Policy (SDP) is optimized with a reinforcement learning (RL) algorithm. Furthermore, to address the insufficient perception of dynamic spatiotemporal features in microgravity, we propose the statedependent latency injection (SDLI) mechanism, which mimics biological neural delays to dynamically regulate the timing of input information. Evaluation on five representative intra-vehicular daily tasks (e.g., hatch opening and precision container capping) shows that our method consistently achieves higher success rates and lower energy consumption, compared to the state-of-the-art robotic manipulation methods. These results demonstrate our method is a viable intra-vehicular robotic manipulation method.

Related papers

• Encoding Predictability and Legibility for Style-Conditioned Diffusion Policy [1.1549572298362782]
  Striking a balance between efficiency and transparent motion is a core challenge in human-robot collaboration.<n>We propose Style-Conditioned Diffusion Policy (SCDP), a modular framework that constrains a pre-trained diffusion model toward either legibility or efficiency.<n>We evaluate SCDP on manipulation and navigation tasks, and results show that it enhances legibility in ambiguous settings while preserving optimal efficiency when legibility is unnecessary.
  arXiv  Detail & Related papers  (2026-03-17T10:55:44Z)
• ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation [9.708461585583791]
  Collaborative transportation of heavy payloads via loco-manipulation is a challenging yet essential capability for legged robots.<n>This study proposes an Alternating Direction Method of Multipliers-based distributed model predictive control framework for loco-manipulation.
  arXiv  Detail & Related papers  (2026-03-07T08:06:51Z)
• Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control [0.9916299448089259]
  This paper introduces a novel framework combining population-coded Spiking Neural Networks (SNNs) with Deep Reinforcement Learning (DRL)<n>We show that our approach achieves energy savings of up to 96.10% compared to traditional Artificial Neural Networks (ANNs) while maintaining comparable control performance.<n>These results position population-coded SNNs as a promising solution for energy-efficient, high-performance robotic control in resource-constrained applications.
  arXiv  Detail & Related papers  (2025-10-12T09:27:25Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories.<n>The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation [80.71541671907426]
  OneStep Diffusion Policy (OneDP) is a novel approach that distills knowledge from pre-trained diffusion policies into a single-step action generator. OneDP significantly accelerates response times for robotic control tasks.
  arXiv  Detail & Related papers  (2024-10-28T17:54:31Z)
• Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning [0.0]
  We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios.
  arXiv  Detail & Related papers  (2024-08-07T14:32:41Z)
• Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression [53.33734159983431]
  This paper introduces a novel approach to distill neural RL policies into more interpretable forms. We train expert neural network policies using RL and distill them into (i) GBMs, (ii) EBMs, and (iii) symbolic policies.
  arXiv  Detail & Related papers  (2024-03-21T11:54:45Z)
• Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
  In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability. We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
  arXiv  Detail & Related papers  (2024-02-04T15:54:03Z)
• Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications [11.010530034121224]
  We introduce a novel Deep Q-learning algorithm that significantly improves learning speed.<n>The enhanced sample efficiency stems from a mission-driven exploration strategy that prioritizes exploration towards directions likely to contribute to mission success.
  arXiv  Detail & Related papers  (2023-11-28T18:59:58Z)
• Navigation of micro-robot swarms for targeted delivery using reinforcement learning [0.0]
  We use the Reinforcement Learning (RL) algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization (RPO) to navigate a swarm of 4, 9 and 16 microswimmers. We look at both PPO and RPO performances with limited state information scenarios and also test their robustness for random target location and size.
  arXiv  Detail & Related papers  (2023-06-30T12:17:39Z)
• Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation [72.24964965882783]
  Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error.<n>Real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and suboptimal policies.<n>We introduce Confidence-Controlled Exploration (CCE), a novel method that improves sample efficiency in RL-based robotic navigation without modifying the reward function.
  arXiv  Detail & Related papers  (2023-06-09T18:45:15Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

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.