The More the Merrier: Running Multiple Neuromorphic Components On-Chip for Robotic Control

• URL: http://arxiv.org/abs/2602.13747v1
• Date: Sat, 14 Feb 2026 12:39:15 GMT
• Title: The More the Merrier: Running Multiple Neuromorphic Components On-Chip for Robotic Control
• Abstract summary: We show a first example of a pipeline for vision-based robot control in which numerous networks can be run entirely on hardware.<n>We show that all components can run concurrently on-chip in the milli Watt regime at latencies competitive with the state-of-the-art.<n>An equivalent network on simulated hardware is shown to accomplish robotic arm plug insertion in simulation, and the core elements of the pipeline are additionally tested on a real robotic arm.
• Score: 0.9503420085198421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has long been realized that neuromorphic hardware offers benefits for the domain of robotics such as low energy, low latency, as well as unique methods of learning. In aiming for more complex tasks, especially those incorporating multimodal data, one hurdle continuing to prevent their realization is an inability to orchestrate multiple networks on neuromorphic hardware without resorting to off-chip process management logic. To address this, we show a first example of a pipeline for vision-based robot control in which numerous complex networks can be run entirely on hardware via the use of a spiking neural state machine for process orchestration. The pipeline is validated on the Intel Loihi 2 research chip. We show that all components can run concurrently on-chip in the milli Watt regime at latencies competitive with the state-of-the-art. An equivalent network on simulated hardware is shown to accomplish robotic arm plug insertion in simulation, and the core elements of the pipeline are additionally tested on a real robotic arm.

Related papers

• Low-power analogue neural networks with trainable nonlinear connections for continuous control [54.59241649882409]
  Physical neural networks promise low-power machine learning by computing directly with analogue device physics.<n>We place trainable nonlinear functions on the connections, making each physical connection a learnable computational element.<n>We find that the benefit is task-dependent and follows from the smoothness of the physical basis.
  arXiv  Detail & Related papers  (2026-06-21T15:55:23Z)
• Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks [53.12548325396181]
  Tendon drives paired with soft muscle actuation enable faster and safer robots.<n>We propose a sim-to-real pipeline that learns a neural network model of this complex actuation.<n>We successfully deploy precise goal-reaching and dynamic ball-in-a-cup policies trained entirely in simulation.
  arXiv  Detail & Related papers  (2026-04-10T16:52:54Z)
• Neuro-Channel Networks: A Multiplication-Free Architecture by Biological Signal Transmission [0.0]
  The rapid proliferation of Deep Learning is constrained by its heavy reliance on high-performance hardware.<n>These specialized accelerators are not only prohibitively expensive and energy-intensive but also suffer from significant supply scarcity.<n>We propose Neuro-Channel Networks (NCN), a novel multiplication-free architecture designed to decouple AI from expensive hardware dependencies.
  arXiv  Detail & Related papers  (2026-01-05T16:33:13Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis [102.1876259853457]
  We propose a tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX. RoboCodeX decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints. To further enhance the capability to map conceptual and perceptual understanding into control commands, a specialized multimodal reasoning dataset is collected for pre-training and an iterative self-updating methodology is introduced for supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-02-25T15:31:43Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Fully neuromorphic vision and control for autonomous drone flight [5.358212984063069]
  Event-based vision and spiking neural hardware promises to exhibit similar characteristics. Here, we present a fully learned neuromorphic pipeline for controlling a drone flying. Results illustrate the potential of neuromorphic sensing and processing for enabling smaller network per flight.
  arXiv  Detail & Related papers  (2023-03-15T17:19:45Z)
• 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)
• A toolbox for neuromorphic sensing in robotics [4.157415305926584]
  We introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI.
  arXiv  Detail & Related papers  (2021-03-03T23:22:05Z)
• Robust trajectory generation for robotic control on the neuromorphic research chip Loihi [0.0]
  We exploit a recently developed spiking neural network model, the so-called anisotropic network. We show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements.
  arXiv  Detail & Related papers  (2020-08-26T16:02:39Z)
• One-step regression and classification with crosspoint resistive memory arrays [62.997667081978825]
  High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge. One-step learning is supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. Results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.
  arXiv  Detail & Related papers  (2020-05-05T08:00:07Z)
• Exposing Hardware Building Blocks to Machine Learning Frameworks [4.56877715768796]
  We focus on how to design topologies that complement such a view of neurons as unique functions. We develop a library that supports training a neural network with custom sparsity and quantization.
  arXiv  Detail & Related papers  (2020-04-10T14:26:00Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)

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.