Archived
2026-09-09TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, et al.
arXiv:2609.09158v1Today’s standout paper is TANGO, a new vision-language navigation system for humanoid robots. The problem is harder than ordinary robot navigation: a humanoid moving through a cluttered room can’t just plan a 2D path, it has to coordinate its whole body in real time, shifting its torso, placing its arms, and adjusting its gait to avoid collisions. TANGO learns this directly from natural-language instructions and egocentric camera views, predicting low-level joint actions for 29 degrees of freedom. The authors train it entirely in simulation, but they don’t just collect random motion; they synthesize realistic, collision-free traversal behaviors using path planning, whole-body motion generation, obstacle-aware edits, and reinforcement learning. What makes this especially compelling is that the same policy transfers zero-shot to a real Unitree G1 humanoid and still works in cluttered indoor scenes. That suggests a practical route toward robots that can understand instructions and physically navigate human environments with much more flexibility.
Also spotted that day
Learning Length-Extrapolatable Recurrent ModelsReCite: Agentic Reasoning for Faithful CitationProcedural Graphs: Self-Evolving Execution Structures for LLM AgentsSilver Rate Is (Almost) Optimal for Gradient Descent AccelerationCopying explains the collective behavior of AI agents in the wildStudying Image Tokenizers as Visual Languages in Unified Multimodal ModelsNOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and ForecastingA Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare ProcessesEntropy-Regularized Rank-Masked Policy Optimization for Test-Time Reinforcement Learning in Code GenerationCo-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation FailsExecCritic: Learn to Test, Test to Improve for Coding AgentsNearly Tight Rademacher Bounds for Sparsely Activated Neural NetworksA Generalization of Amari's Bayesian DualityCanonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs
Previous daily papers
2026-09-26PoEM: Predicting RL Outcomes from Existing Policies2026-09-26LLM Agents Can Easily Tamper With Their Own Traces2026-09-26Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure2026-09-25TrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations2026-09-24Frozen Flows Forget: Diagnosing and Restoring Lost Motion in a Latent-flow World Model2026-09-24StudentBench: AI and human tutoring yield equivalent GRE learning gains2026-09-24Agent-Editing World Model: Rethinking World Modeling for LLM Agents2026-09-23Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents2026-09-23FleXray: Universal Clinical X-ray Segmentation2026-09-23SWE-Serve: Benchmarking Agentic Engineering For Production Inference Serving2026-09-22GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay2026-09-22Emergent Collusion in Long-Horizon LLM Agent Interaction2026-09-22WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory2026-09-21Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw2026-09-21Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design2026-09-21CodeMidas: Scaling Agentic Coding RL Environments from Code Itself2026-09-20RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning2026-09-20Quantifying Overclaiming Propensity in Frontier LLM Agents2026-09-20JEPA-Anything: Learning Predictive Models across Different Worlds2026-09-19An Empirical Study of Harness Design for Coding Agents2026-09-17rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference2026-09-17How Model Growth, Recursion, and Boundary Operators Influence Scaling Exponents2026-09-17Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations2026-09-16JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management2026-09-16Agentic Societies Need a Social Harness2026-09-16Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback2026-09-15Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection2026-09-14A Ranking Approach for Measuring Calibration2026-09-12GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay2026-09-12CausalArena: Benchmarking Causal Discovery in the Foundation Model Era2026-09-11Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data2026-09-11General Quantification of Covariate and Concept Shifts2026-09-10Show-Harness: Just a VLM Agent Can Play Robots2026-09-10JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition2026-09-10Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning2026-09-09ExecCritic: Learn to Test, Test to Improve for Coding Agents2026-09-09Learning Length-Extrapolatable Recurrent Models2026-09-08WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data2026-09-08UniMate: One Unified Model to Animate Diverse Skeletons2026-09-08CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents2026-09-06Compile by Training: Turning Natural-Language Specifications into Local Neural Functions2026-09-06Rethinking On-Policy Distillation of Large Language Models II: One Training Example2026-09-06SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents2026-09-05From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research2026-09-05Last Translation Benchmark2026-09-03A Common Measure of Communication for Speech Brain-Computer Interfaces2026-09-03Discriminative World Models for Web Agents2026-09-03Post-Training Language Models for Gold-Medal Performance in Coding Competitions2026-09-02Mechanism Design for Alignment and Control2026-09-02Beyond Scores: Understanding LLM-as-a-Judge Mechanisms in Summarization Evaluation2026-09-02The Rise of Verbal Reinforcement Learning2026-09-01Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions2026-09-01Aspire: Can Models Self-Evolve from Vague Goals?2026-09-01PaperGym: Rubric-Centered Evolution for Research-Plan Generation2026-08-31DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging2026-08-31Video Generative Models as Geometry Learner2026-08-31When Robots Mishear Us: Mapping the Safety Risks of Voice-Controlled Embodied AI2026-08-30CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes2026-08-30Boosting LLM Exploration via Weak-Model Guidance in RLVR