A Hetero-Associative Sequential Memory Model Utilizing Neuromorphic Signals: Validated on a Mobile Manipulator

• URL: http://arxiv.org/abs/2512.07032v1
• Date: Sun, 07 Dec 2025 22:50:01 GMT
• Title: A Hetero-Associative Sequential Memory Model Utilizing Neuromorphic Signals: Validated on a Mobile Manipulator
• Abstract summary: This paper presents a hetero-associative sequential memory system for mobile manipulators.<n>It learns compact, neuromorphic bindings between robot joint states and tactile observations.<n>Results demonstrate single-joint and full-arm behaviors executed via associative recall.
• Score: 3.1187972486989906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a hetero-associative sequential memory system for mobile manipulators that learns compact, neuromorphic bindings between robot joint states and tactile observations to produce step-wise action decisions with low compute and memory cost. The method encodes joint angles via population place coding and converts skin-measured forces into spike-rate features using an Izhikevich neuron model; both signals are transformed into bipolar binary vectors and bound element-wise to create associations stored in a large-capacity sequential memory. To improve separability in binary space and inject geometry from touch, we introduce 3D rotary positional embeddings that rotate subspaces as a function of sensed force direction, enabling fuzzy retrieval through a softmax weighted recall over temporally shifted action patterns. On a Toyota Human Support Robot covered by robot skin, the hetero-associative sequential memory system realizes a pseudocompliance controller that moves the link under touch in the direction and with speed correlating to the amplitude of applied force, and it retrieves multi-joint grasp sequences by continuing tactile input. The system sets up quickly, trains from synchronized streams of states and observations, and exhibits a degree of generalization while remaining economical. Results demonstrate single-joint and full-arm behaviors executed via associative recall, and suggest extensions to imitation learning, motion planning, and multi-modal integration.

Related papers

• Rethinking Memory as Continuously Evolving Connectivity [63.58181883607843]
  Existing memory-augmented LLM agents treat memory as a static repository with pre-defined representations and fixed retrieval pipelines.<n>We propose FluxMem, a connectivity-evolving memory framework that models memory as a heterogeneous graph and progressively refines its topology through three stages.<n>Across three fundamentally distinct benchmarks including LoCoMo, Mind2Web, and GAIA, FluxMem achieves consistent state-of-the-art performance, demonstrating strong adaptation and generalization in complex agentic environments.
  arXiv  Detail & Related papers  (2026-05-27T17:35:34Z)
• Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN [62.429669462281645]
  6G must transcend traditional automation to embrace agentic AI capable of perception, reasoning, and evolution.<n>This article envisions a shift from interface-bound to memory-centric architectures.<n>By replacing message passing with zero-copy observability, we empower AI agents to bridge the gap between real-time responsiveness and long-horizon context.
  arXiv  Detail & Related papers  (2026-05-11T06:04:10Z)
• Dual Latent Memory for Visual Multi-agent System [69.29799381195592]
  Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration.<n>Increasing agent turns often degrades performance while exponentially inflating token costs.<n>We propose L$2$-VMAS, a novel model-agnostic framework that enables inter-agent collaboration with dual latent memories.
  arXiv  Detail & Related papers  (2026-01-31T02:49:10Z)
• SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation [29.545442480332515]
  We introduce Synapse, a unified memory architecture that transcends static rather than pre-computed links.<n>We show that Synapse significantly outperforms state-of-the-art methods in complex temporal and multi-hop reasoning tasks.<n>Our code and data will be made publicly available upon acceptance.
  arXiv  Detail & Related papers  (2026-01-06T06:19:58Z)
• Towards Arbitrary Motion Completing via Hierarchical Continuous Representation [64.6525112550758]
  We propose a novel parametric activation-induced hierarchical implicit representation framework, called NAME, based on Implicit Representations (INRs)<n>Our method introduces a hierarchical temporal encoding mechanism that extracts features from motion sequences at multiple temporal scales, enabling effective capture of intricate temporal patterns.
  arXiv  Detail & Related papers  (2025-12-24T14:07:04Z)
• Egocentric Visual Navigation through Hippocampal Sequences [0.0]
  We show that hippocampal sequences arise from intrinsic recurrent circuitry that propagates activity without readily available input.<n>We implement a minimal sequence generator inspired by neurobiology and pair it with an actor-critic learner for egocentric visual navigation.
  arXiv  Detail & Related papers  (2025-10-11T01:38:23Z)
• Wave-Based Semantic Memory with Resonance-Based Retrieval: A Phase-Aware Alternative to Vector Embedding Stores [51.56484100374058]
  We propose a novel framework that models knowledge as wave patterns $psi(x) = A(x) eiphi(x)$ and retrieves it through resonance-based interference.<n>This approach preserves both amplitude and phase information, enabling more expressive and robust semantic similarity.
  arXiv  Detail & Related papers  (2025-08-21T10:13:24Z)
• Higher-Order Kuramoto Oscillator Network for Dense Associative Memory [0.0]
  We show that higher-order couplings achieve superlinear scaling of memory capacity with system size.<n>These results bridge the Kuramoto synchronization with modern Hopfield memories.
  arXiv  Detail & Related papers  (2025-07-29T16:35:52Z)
• Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model [55.116403765330084]
  Current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. We propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros.
  arXiv  Detail & Related papers  (2024-04-08T16:34:35Z)
• Emulating Complex Synapses Using Interlinked Proton Conductors [17.304569471460013]
  We experimentally realize the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.
  arXiv  Detail & Related papers  (2024-01-26T18:16:06Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Sparse Coding in a Dual Memory System for Lifelong Learning [13.041607703862724]
  Brain efficiently encodes information in non-overlapping sparse codes. We employ sparse coding in a multiple-memory replay mechanism. Our method maintains an additional long-term semantic memory that aggregates and consolidates information encoded in the synaptic weights of the working model.
  arXiv  Detail & Related papers  (2022-12-28T12:56:15Z)
• Continuous and time-discrete non-Markovian system-reservoir interactions: Dissipative coherent quantum feedback in Liouville space [62.997667081978825]
  We investigate a quantum system simultaneously exposed to two structured reservoirs. We employ a numerically exact quasi-2D tensor network combining both diagonal and off-diagonal system-reservoir interactions with a twofold memory for continuous and discrete retardation effects. As a possible example, we study the non-Markovian interplay between discrete photonic feedback and structured acoustic phononovian modes, resulting in emerging inter-reservoir correlations and long-living population trapping within an initially-excited two-level system.
  arXiv  Detail & Related papers  (2020-11-10T12:38:35Z)

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.