HALO distills VLM priors via question-answering objectives and applies sparse attention to enable reliable memory retrieval from up to eight minutes of history in imitation-learned visuomotor policies.
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34 Pith papers cite this work. Polarity classification is still indexing.
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2026 34representative citing papers
AURA-Mem uses an action-gated recurrent memory trained on closed-loop action error to deliver constant 4,224-byte state and 5-9x fewer writes than baselines while matching base policy success on LIBERO-Long.
ECHO organizes VLA experiences into a hierarchical memory tree in hyperbolic space via autoencoder and entailment constraints, delivering a 12.8% success-rate gain on LIBERO-Long over the pi0 baseline.
π₀.₇ is a steerable generalist robotic model that uses rich multimodal prompts including language, subgoal images, and performance metadata to achieve out-of-the-box generalization across tasks and robot bodies.
PhysMem enables VLM-based robot planners to learn and verify physical properties through test-time interaction and hypothesis testing, raising success on a brick insertion task from 23% to 76%.
Injecting a liquid-time-constant task-progress belief into the action decoder of a VLA policy improves stage-dependent manipulation success, especially under occlusion and visual perturbation.
Repurposing a VLA’s vision encoder to emit one action-supervised memory token per historical frame-view yields long-horizon manipulation with large success gains and low latency.
FPL trains a language-conditioned reward model from per-axis human preferences and a reward-conditioned policy, reporting 38-point average success gains over sparse-reward and binary-preference baselines on six manipulation tasks.
KEMO is an event-driven keyframe memory system that improves VLA policy success rates by 23.6% on real dual-arm tasks by selectively preserving task-relevant history via kinematics-visual event detection and gated fusion.
RoboMME-Interference measures VLA memory under cross-session interference and finds that all tested systems decay to near their no-memory baseline as unrelated sessions accumulate.
w²VLA restructures VLA information flow to decouple declarative semantics from procedural skills, enabling zero-shot transfer to novel objects.
CAMP learns a compressed behavioral memory from action history to enable success in long-horizon partially observable object manipulation without extra supervision, showing gains over baselines in real-robot and simulation tests.
Vesta is a unified embodied generalist model that outperforms specialist baselines by over 20% on average and improves real-world robotic task success by over 35%.
EventVLA introduces foundational visual anchors and a Keyframe Evidence Memory module that predicts future keyframe probabilities from VLA embeddings to improve long-horizon task success by an average of 40% on 17 simulation and 4 real-world tasks.
SERF conditions VLA policies on online-updated neural point maps of environment and robot to improve long-horizon mobile manipulation on BEHAVIOR-1K.
DAM-VLA decouples per-modality temporal processing in vision-language-action models via latent buffers refreshed at sensor rates, achieving 95.2% average success versus 40.95% for synchronous baselines on seven real-world manipulation tasks while enabling 100 Hz control.
Adding recurrent memory tokens to VLA models raises success rates on partially observable manipulation tasks from 0.42 to 0.84 on training and 0.07 to 0.23 on held-out tasks while preserving performance under full observability.
VLA models with inference-time steering mitigate action leakage in implicit human-robot collaboration, supporting longer horizons and yielding faster, more reliable assembly than shorter-horizon baselines in a 16-person study.
Hide-and-Seek uses contrastive objectives on trajectories to localize failure signals in VLA models from trajectory-level supervision alone.
RoboMemArena is a new large-scale robotic memory benchmark with real-world tasks, and PrediMem is a dual VLA system that outperforms baselines by managing memory buffers with predictive coding.
ExoActor uses exocentric video generation to implicitly model robot-environment-object interactions and converts the resulting videos into task-conditioned humanoid control sequences.
CLWM with DINOv3 targets, O(1) TTT memory, SAI latency masking, and EmbodiChain training achieves SOTA dual-arm simulation performance and zero-shot sim-to-real transfer that beats real-data finetuned baselines.
A dual-system framework with a structured subtask interface, event-balanced training, and inference harness enables VLM-guided long-horizon robotic manipulation, achieving 95.5% on LIBERO-Long and 65% on real-world chemistry tasks.
Multi-bank similarity-merged event memory plus progress supervision raises long-horizon WAM success from 28.4% to 69.8% on RMBench and full-task success from 52.5% to 80% on real Franka tasks.
citing papers explorer
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Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control
HALO distills VLM priors via question-answering objectives and applies sparse attention to enable reliable memory retrieval from up to eight minutes of history in imitation-learned visuomotor policies.
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AURA: Action-Gated Memory for Robot Policies at Constant VRAM
AURA-Mem uses an action-gated recurrent memory trained on closed-loop action error to deliver constant 4,224-byte state and 5-9x fewer writes than baselines while matching base policy success on LIBERO-Long.
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ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
ECHO organizes VLA experiences into a hierarchical memory tree in hyperbolic space via autoencoder and entailment constraints, delivering a 12.8% success-rate gain on LIBERO-Long over the pi0 baseline.
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${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
π₀.₇ is a steerable generalist robotic model that uses rich multimodal prompts including language, subgoal images, and performance metadata to achieve out-of-the-box generalization across tasks and robot bodies.
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PhysMem: Scaling Test-Time Memory for Embodied Physical Reasoning
PhysMem enables VLM-based robot planners to learn and verify physical properties through test-time interaction and hypothesis testing, raising success on a brick insertion task from 23% to 76%.
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TFP: Temporally Conditioned Memory-Fusion Policies for Visuomotor Learning
Injecting a liquid-time-constant task-progress belief into the action decoder of a VLA policy improves stage-dependent manipulation success, especially under occlusion and visual perturbation.
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NativeMEM: Native Memory Compression for Long-Horizon Robotic Manipulation
Repurposing a VLA’s vision encoder to emit one action-supervised memory token per historical frame-view yields long-horizon manipulation with large success gains and low latency.
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Freeform Preference Learning for Robotic Manipulation
FPL trains a language-conditioned reward model from per-axis human preferences and a reward-conditioned policy, reporting 38-point average success gains over sparse-reward and binary-preference baselines on six manipulation tasks.
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KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies
KEMO is an event-driven keyframe memory system that improves VLA policy success rates by 23.6% on real dual-arm tasks by selectively preserving task-relevant history via kinematics-visual event detection and gated fusion.
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RoboMME-Interference: Benchmarking Robot Memory Under Interference
RoboMME-Interference measures VLA memory under cross-session interference and finds that all tested systems decay to near their no-memory baseline as unrelated sessions accumulate.
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Decoupling the Declarative from the Procedural in Vision-Language-Action Models
w²VLA restructures VLA information flow to decouple declarative semantics from procedural skills, enabling zero-shot transfer to novel objects.
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Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation
CAMP learns a compressed behavioral memory from action history to enable success in long-horizon partially observable object manipulation without extra supervision, showing gains over baselines in real-robot and simulation tests.
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Vesta: A Generalist Embodied Reasoning Model
Vesta is a unified embodied generalist model that outperforms specialist baselines by over 20% on average and improves real-world robotic task success by over 35%.
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EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies
EventVLA introduces foundational visual anchors and a Keyframe Evidence Memory module that predicts future keyframe probabilities from VLA embeddings to improve long-horizon task success by an average of 40% on 17 simulation and 4 real-world tasks.
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SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation
SERF conditions VLA policies on online-updated neural point maps of environment and robot to improve long-horizon mobile manipulation on BEHAVIOR-1K.
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DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model
DAM-VLA decouples per-modality temporal processing in vision-language-action models via latent buffers refreshed at sensor rates, achieving 95.2% average success versus 40.95% for synchronous baselines on seven real-world manipulation tasks while enabling 100 Hz control.
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$\mu$VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models
Adding recurrent memory tokens to VLA models raises success rates on partially observable manipulation tasks from 0.42 to 0.84 on training and 0.07 to 0.23 on held-out tasks while preserving performance under full observability.
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Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration
VLA models with inference-time steering mitigate action leakage in implicit human-robot collaboration, supporting longer horizons and yielding faster, more reliable assembly than shorter-horizon baselines in a 16-person study.
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Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
Hide-and-Seek uses contrastive objectives on trajectories to localize failure signals in VLA models from trajectory-level supervision alone.
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RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark
RoboMemArena is a new large-scale robotic memory benchmark with real-world tasks, and PrediMem is a dual VLA system that outperforms baselines by managing memory buffers with predictive coding.
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ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control
ExoActor uses exocentric video generation to implicitly model robot-environment-object interactions and converts the resulting videos into task-conditioned humanoid control sequences.
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DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks
CLWM with DINOv3 targets, O(1) TTT memory, SAI latency masking, and EmbodiChain training achieves SOTA dual-arm simulation performance and zero-shot sim-to-real transfer that beats real-data finetuned baselines.
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Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
A dual-system framework with a structured subtask interface, event-balanced training, and inference harness enables VLM-guided long-horizon robotic manipulation, achieving 95.5% on LIBERO-Long and 65% on real-world chemistry tasks.
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DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
Multi-bank similarity-merged event memory plus progress supervision raises long-horizon WAM success from 28.4% to 69.8% on RMBench and full-task success from 52.5% to 80% on real Franka tasks.
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World Value Models for Robotic Manipulation
World Value Model (WVM) integrates world models with value estimation to achieve SOTA Value-Order Correlation on expert and suboptimal robotic data and improves downstream policy performance.
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FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
FOCA improves few-shot VLA adaptation by explicitly predicting future interaction embeddings and implicitly aligning to goal observations, yielding up to 26% gains on real robots with only 20 demonstrations.
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MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation
MemoryVAM integrates a Perceiver-based Recap Compressor and Cue Gate into video action models, raising success rates on long-horizon manipulation from 5% to 42.5% on LIBERO-Mem and 75-80% on real-robot counting, spatial recall, and tracking tasks.
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Real-Time Execution with Autoregressive Policies
Autoregressive VLA policies achieve real-time execution via tokenization horizon adjustment and constrained decoding, outperforming flow-matching policies in speed and performance across simulated and real environments.
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MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models
MemoryVLA++ integrates a perceptual-cognitive memory bank and denoising world model into VLA models to enable temporal reasoning, yielding performance gains on manipulation benchmarks and real-robot tasks.
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Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
A dual VLM-VLA framework for long-horizon robot manipulation achieves 32.4% success on RMBench tasks versus 9.8% for the strongest baseline via structured memory and closed-loop adaptive replanning.
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MemoryWAM: Efficient World Action Modeling with Persistent Memory
MemoryWAM is a world action model with a hybrid memory design using recent frames, anchor frames, and gist tokens for efficient long-horizon robotic manipulation.
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Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models
An 8B embodied foundation model trained on 15B tokens with multi-task RL and a Planner-Grounder-Corrector loop claims SOTA on 16/24 embodied VLM benchmarks and strong VLA/real-robot transfer.
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Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation
A structured literature survey of safety mechanisms in long-horizon robotic manipulation organized by intervention timing and strength of supporting evidence.
- Action-Effect Memory Pretraining for Robot Manipulation