SOLO: Stable Omni-terrain Long-Horizon Perceptive Humanoid Locomotion

• URL: http://arxiv.org/abs/2608.26583v2
• Date: Mon, 31 Aug 2026 05:04:56 GMT
• Title: SOLO: Stable Omni-terrain Long-Horizon Perceptive Humanoid Locomotion
• Abstract summary: We present SOLO, a unified framework addressing two compounding causes of long-horizon fragility: dense terrain reconstruction smooths action-critical details, and pointwise imitation lacks temporal credit assignment.<n>Its Query Reconstructor (QR) uses Fourier-encoded cell queries to retrieve spatially specific evidence from depth-proprioception tokens, preserving sharp terrain boundaries.<n>Its Trajectory-Aware MSE (TA-MSE) Distillation adds next-state teacher-student disagreement to the PPO reward, enabling Generalized Advantage Estimation to propagate future disagreement penalties to preceding actions.
• Score: 41.64988244398264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans traverse complex terrain over long distances without losing balance, whereas perceptive humanoid policies become fragile as perception and control errors accumulate. We present SOLO, a unified framework addressing two compounding causes of this long-horizon fragility: dense terrain reconstruction smooths action-critical details, and pointwise imitation lacks temporal credit assignment. Its Query Reconstructor (QR) uses Fourier-encoded cell queries to retrieve spatially specific evidence from depth-proprioception tokens, preserving sharp terrain boundaries. Trajectory-Aware MSE (TA-MSE) Distillation adds next-state teacher-student disagreement to the PPO reward, enabling Generalized Advantage Estimation to propagate future disagreement penalties to preceding actions. In simulation, QR reduces height-map L1 error by factors of 3.3-4.0, while TA-MSE surpasses PPO and MSE+PPO in curriculum progression. On stress-test terrains, SOLO achieves 97.5% mean traversal success and 96% stepping-stone success, versus 75.0-75.6% and 0-3% for dense-reconstructor variants. Deployed zero-shot with only a chest-mounted depth camera and proprioception, SOLO completes a continuous 1.5-km outdoor route and an indoor mixed-terrain course. Project page: https://sunpihai-up.github.io/solo/

Related papers

• World-Model-Augmented Visual Locomotion for Humanoids on Foothold-Constrained Terrain [29.978527754674044]
  We ask whether a learned predictive summary of near-future observations and rewards can provide the anticipatory information required in such settings.<n>We present World-Model-Augmented Visual Locomotion (WM-LOCO), which jointly trains a recurrent world model and a PPO policy.<n>In simulation, WM-LOCO succeeds on gaps and stepping stones where a matched baseline fails completely, and matches the baseline's success rate on stairs.
  arXiv  Detail & Related papers  (2026-09-02T12:57:01Z)
• Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents [50.85250898062218]
  Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol.<n>A natural alternative is to let the agent emit variable-length action chunks.<n>But naively training such policies with standard reinforcement learning fails.
  arXiv  Detail & Related papers  (2026-09-02T03:17:11Z)
• Scaffolding Foundation Models into Physical-World Agents Pushes the Frontier of Long-Horizon Navigation [52.90741218586653]
  Long-horizon physical-world agents must reason over distant goals while grounding decisions in reliable closed-loop behavior.<n>We introduce NavMCP, an agentic scaffolding framework that couples a VLM reasoning agent with an NFM executor for long-horizon exploration.<n>NavMCP achieves state-of-the-art results on Embodied Question Answering and a Unitree Go2.
  arXiv  Detail & Related papers  (2026-08-31T07:50:16Z)
• SSTG-Nav: Metric-Grounded Spatial-Semantic Topological Graphs for Reusable Object Navigation [2.272547247667044]
  SSTG-Nav is a reusable metric-semantic memory that turns a one-time survey into actionable object goals.<n>On 1,000 HM3D-v2 episodes across 36 scenes, our goal-independent topology achieves a 99.4% geometric success ceiling.
  arXiv  Detail & Related papers  (2026-08-01T08:40:04Z)
• CosFly-VLA: A Spatially Aware Vision-Language-Action Model for UAV Tracking [6.465600276213322]
  Vision-Language-Action (VLA) policies can track visible targets effectively, but their performance often degrades when buildings, vegetation, or roadside objects block the line of sight.<n>We present CosFly-VLA, a spatially aware VLA model that jointly grounds the target, estimates its visibility, and generates continuous flight actions.<n>Relative to OpenVLA, CosFly-VLA-0.8B reduces open-loop Average Displacement Error (ADE) by 34.1% on seen-test and 35.3% on unseen-test.
  arXiv  Detail & Related papers  (2026-07-16T13:52:08Z)
• The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents [0.0]
  Intervention timing is a low-reliability construct, making single-annotator F1 an unsuitable optimization target.<n>Our contribution is the joint mapping of this problem across human inter-rater reliability, four detector architectures, a cross-model LLM-judge sweep, and a reproduced saturation effect.
  arXiv  Detail & Related papers  (2026-06-02T23:54:27Z)
• Milestone-Guided Policy Learning for Long-Horizon Language Agents [67.87164829944238]
  We introduce a milestone-guided policy learning framework, BEACON, to train long-horizon language agents.<n>On ALFWorld, WebShop, and ScienceWorld, BEACON consistently outperforms GRPO and GiGPO.<n>These results establish milestone-anchored credit assignment as an effective paradigm for training long-horizon language agents.
  arXiv  Detail & Related papers  (2026-05-07T12:00:40Z)
• Beyond the Attention Stability Boundary: Agentic Self-Synthesizing Reasoning Protocols [6.357772907811544]
  Self- Synthesizing Reasoning Protocols (SSRP) is a metacognitive framework that implements a discrete separation between architectural planning and procedural execution.<n>We present 3 experimental tiers: a shallow recency-based retrieval pilot, a high-entropy SOP, and a semantic hijacked 3-hop Multi-Fact Synthesis task.<n>Our results empirically locate the Attention Stability Boundary, where stateless Vanilla ReAct baselines for GPT 5.4 collapse to 0.1% success while SSRP achieves a 715X Resilience Lift.
  arXiv  Detail & Related papers  (2026-04-27T14:13:30Z)
• BadCLIP++: Stealthy and Persistent Backdoors in Multimodal Contrastive Learning [73.46118996284888]
  Research on backdoor attacks against multimodal contrastive learning models faces two key challenges: stealthiness and persistence.<n>We propose BadCLIP++, a unified framework that tackles both challenges.<n>For stealthiness, we introduce a semantic-fusion QR micro-trigger that embeds imperceptible patterns near task-relevant regions.<n>For persistence, we stabilize trigger embeddings via radius shrinkage and centroid alignment.
  arXiv  Detail & Related papers  (2026-02-19T08:31:16Z)
• Knowing the Answer Isn't Enough: Fixing Reasoning Path Failures in LVLMs [85.37131922131657]
  We reveal a critical yet underexplored flaw in Large Vision-Language Models (LVLMs)<n>Even when these models know the correct answer, they frequently arrive there through incorrect reasoning paths.<n>We propose PSO (Path-Select Optimization), a two-stage post-training framework designed to enhance both the reasoning performance and stability of existing LVLMs.
  arXiv  Detail & Related papers  (2025-12-06T03:02:55Z)
• On GRPO Collapse in Search-R1: The Lazy Likelihood-Displacement Death Spiral [59.14787085809595]
  We identify Lazy Likelihood Displacement (LLD) as the core mechanism driving this failure.<n>LLD emerges early and triggers a self-reinforcing LLD Death Spiral, where declining likelihood leads to low-confidence responses.<n>We propose a lightweight likelihood-preserving regularization LLDS for GRPO that activates only when a trajectory's likelihood decreases.
  arXiv  Detail & Related papers  (2025-12-03T19:41:15Z)
• Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail [85.47497935739936]
  Alpamayo-R1 (AR1) is a vision-language-action model that integrates Chain of Causation reasoning with trajectory planning.<n>We show AR1 achieves 12% improvement in planning accuracy on challenging cases compared to a trajectory-only baseline.<n>We plan to release AR1 models and a subset of the CoC in a future update.
  arXiv  Detail & Related papers  (2025-10-30T01:25:34Z)
• Adversarial Loss for Semantic Segmentation of Aerial Imagery [12.241693880896348]
  We propose a novel loss function that learns to understand both local and global contexts for semantic segmentation. The newly proposed loss function deployed on the DeepLab v3+ network obtains state-of-the-art results on the Massachusetts buildings dataset.
  arXiv  Detail & Related papers  (2020-01-13T14:22:54Z)

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.