Device-Conditioned Neural Architecture Search for Efficient Robotic Manipulation

• URL: http://arxiv.org/abs/2604.10170v1
• Date: Sat, 11 Apr 2026 11:36:16 GMT
• Title: Device-Conditioned Neural Architecture Search for Efficient Robotic Manipulation
• Abstract summary: We propose a unified framework named textbfDevice-textbfConditioned textbfQuantization-textbfFor-textbfAll (DC-QFA)<n>DC-QFA amortizes deployment effort with the device-conditioned quantization-aware training and hardware-constrained architecture search.
• Score: 16.017058636822025
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The growing complexity of visuomotor policies poses significant challenges for deployment with heterogeneous robotic hardware constraints. However, most existing model-efficient approaches for robotic manipulation are device- and model-specific, lack generalizability, and require time-consuming per-device optimization during the adaptation process. In this work, we propose a unified framework named \textbf{D}evice-\textbf{C}onditioned \textbf{Q}uantization-\textbf{F}or-\textbf{A}ll (DC-QFA) which amortizes deployment effort with the device-conditioned quantization-aware training and hardware-constrained architecture search. Specifically, we introduce a single supernet that spans a rich design space over network architectures and mixed-precision bit-widths. It is optimized with latency- and memory-aware regularization, guided by per-device lookup tables. With this supernet, for each target platform, we can perform a once-for-all lightweight search to select an optimal subnet without any per-device re-optimization, which enables more generalizable deployment across heterogeneous hardware, and substantially reduces deployment time. To improve long-horizon stability under low precision, we further introduce multi-step on-policy distillation to mitigate error accumulation during closed-loop execution. Extensive experiments on three representative policy backbones, such as DiffusionPolicy-T, MDT-V, and OpenVLA-OFT, demonstrate that our DC-QFA achieves $2\text{-}3\times$ acceleration on edge devices, consumer-grade GPUs, and cloud platforms, with negligible performance drop in task success. Real-world evaluations on an Inovo robot equipped with a force/torque sensor further validates that our low-bit DC-QFA policies maintain stable, contact-rich manipulation even under severe quantization.

Related papers

• LUCID: An Agentic AI Framework on Digital-Twin in the Loop for QoS-Guaranteeing Robotic Control [8.429132298367412]
  Cloud robotics relies on the timely uplink of high-volume sensing streams, yet dynamic environments continually shift the feasible combinations of trajectories, active-robot counts, and per-robot combinations.<n>We present LUCID, an uplink-aware cloud-robotics pipeline that moves TP--RRM from solving a fixed formulation to dynamically orchestrating optimization problem schemas.
  arXiv  Detail & Related papers  (2026-08-28T15:19:43Z)
• Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI [1.1666234644810893]
  We introduce deployment-aligned low-precision training directly into hardware-aware neural architecture search (NAS)<n>We evaluate the proposed framework on vessel segmentation for spaceborne maritime monitoring, targeting the Intel Movidius Myriad X Visual Processing Unit (VPU)<n>Results demonstrate that incorporating deployment-consistent numerical constraints into hardware-aware NAS substantially improves robustness and alignment between optimization and deployment for resource-constrained edge Artificial Intelligence (AI)
  arXiv  Detail & Related papers  (2026-04-27T13:58:18Z)
• A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models [9.31160191200499]
  Large Language Models (LLMs) on edge devices face severe computational and memory constraints.<n>We propose a lightweight, backpropagation-free, surrogate-based sensitivity analysis framework to identify hybrid SSM-Transformer components most susceptible to quantization-induced degradation.
  arXiv  Detail & Related papers  (2026-04-15T03:40:30Z)
• LiteVLA-Edge: Quantized On-Device Multimodal Control for Embedded Robotics [0.6119773373677944]
  We present LiteVLA-Edge, a deployment-oriented VLA pipeline for fully on-device inference on Jetson Orin-class hardware.<n>Our approach combines supervised image-to-action fine-tuning in FP32 with post-training 4-bit GGUF quantization and GPU-accelerated inference.<n>Under our configuration, LiteVLA-Edge achieves a mean end-to-end runtime of 150.5,ms (approximately 6.6,Hz) while operating entirely offline.
  arXiv  Detail & Related papers  (2026-03-03T03:20:52Z)
• MOBIUS: Big-to-Mobile Universal Instance Segmentation via Multi-modal Bottleneck Fusion and Calibrated Decoder Pruning [91.90342432541138]
  Scaling up model size and training data has advanced foundation models for instance-level perception.<n>High computational cost limits adoption on resource-constrained platforms.<n>We introduce a new benchmark for efficient segmentation on both high-performance computing platforms and mobile devices.
  arXiv  Detail & Related papers  (2025-10-16T18:00:00Z)
• CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks [57.95170323315603]
  We introduce CollaPipe, a distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving networks.<n>In CollaPipe, the encoder part is adaptively partitioned into variable-sized segments and deployed across mobile devices for pipeline-parallel training, while the decoder is deployed on edge servers to handle generative tasks.<n>To enhance training efficiency, we formulate a joint optimization problem that adaptively allocates model segments, micro-batches, bandwidth, and transmission power.
  arXiv  Detail & Related papers  (2025-09-24T07:54:01Z)
• QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution [53.13952833016505]
  We propose a low-bit quantization model for real-world video super-resolution (VSR)<n>We use a calibration dataset to measure both spatial and temporal complexity for each layer.<n>We refine the FP and low-bit branches to achieve simultaneous optimization.
  arXiv  Detail & Related papers  (2025-08-06T14:35:59Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge [35.40849522296486]
  Large-scale foundation models (FoMos) can perform human-like intelligence. FoMos need to be adapted to specialized downstream tasks through fine-tuning techniques. We advocate multi-device cooperation within the device-edge cooperative fine-tuning paradigm.
  arXiv  Detail & Related papers  (2024-07-13T12:47:14Z)
• Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems [12.427821850039448]
  We propose a novel split computing approach based on slimmable ensemble encoders. The key advantage of our design is the ability to adapt computational load and transmitted data size in real-time with minimal overhead and time. Our model outperforms existing solutions in terms of compression efficacy and execution time, especially in the context of weak mobile devices.
  arXiv  Detail & Related papers  (2023-06-22T06:33:12Z)
• Real-Time GPU-Accelerated Machine Learning Based Multiuser Detection for 5G and Beyond [70.81551587109833]
  nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity. One of the main challenges comes from the real-time implementation of these algorithms. This paper explores the acceleration of APSM-based algorithms through massive parallelization.
  arXiv  Detail & Related papers  (2022-01-13T15:20:45Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Architecture Aware Latency Constrained Sparse Neural Networks [35.50683537052815]
  In this paper, we design an architecture aware latency constrained sparse framework to prune and accelerate CNN models. We also propose a novel sparse convolution algorithm for efficient computation. Our system-algorithm co-design framework can achieve much better frontier among network accuracy and latency on resource-constrained mobile devices.
  arXiv  Detail & Related papers  (2021-09-01T03:41:31Z)

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.