How far is demo capability from deployment economics?
THEME RESEARCH
Embodied AI & Robotics
Will value accrue first to robot data, edge compute, components, or operators that can generate repeat orders?
Paid deployments, intervention rate, uptime, and cost per completed task
01TOP SIGNAL
Abstract screened
Read evidence →Technical Report: One-Step Drifting Action Heads for GR00T N1.7
The takeaway: One-step action generation can substantially reduce the inference cost of vision-language-action (VLA) policies, but its effect on closed-loop task success remains an open question. This technical report studies a GR00T N1.7 variant in which the iterative diffusion-transformer action head is replaced by a one-step drifting action head, together with an overlap-conditioned extension for asynchronous chunk replacement. All multi-seed drifting runs were trained on two NVIDIA A800 GPUs. On LIBERO, the action head reduces the mean model-forward time of the action head from approximately $45.3\,\mathrm{ms}$ to $5.0\,\mathrm{ms}$, while the measured backbone-plus-head time falls from approximately $70.0\,\mathrm{ms}$ to $30.6\,\mathrm{ms}$. However, this speedup is accompanied by a systematic reduction in task success. Across three drifting seeds, success is $64.0\pm4.0\%$ on LIBERO-Spatial, $52.0\pm1.0\%$ on LIBERO-Goal, and $26.0\pm2.6\%$ on LIBERO-Long. The low seed variance indicates that the degradation is not explained by random initialization alone. We report the result as a speed--success trade-off rather than an overall improvement, and discuss likely contributing factors including deterministic one-step mode averaging, batch-dependent geometry estimation, long open-loop chunk execution, and the fact that synchronous LIBERO evaluation does not exercise the asynchronous overlap path.
02
FolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects
Embodied AI, including vision-language-action and world-action models, must operate reliably in the physical world. Yet methods that perform well in simulation can degrade substantially on real robots, especially in long-horizon deformable-object manipulation, where policies must track changing states and execute reliable multi-stage bimanual interactions. Existing real-robot benchmarks mainly focus on short-horizon rigid-object tasks and offer limited coverage of long-horizon deformable manipulation. We introduce FolDeX, a physical-world benchmark built entirely from real-robot data, with garment folding as its primary task. Since real-robot data collection is costly, FolDeX studies how heterogeneous physical experience can be reused efficiently. The benchmark is organized around four research axes: leveraging human intervention and recovery data collected during deployment; transferring data across tasks, including across garment categories and from rigid to deformable-object manipulation; reusing data across scenes with changes in lighting, background, and layout; and transferring data across robotic embodiments. FolDeX provides 2,000+ hours of real-robot data spanning 20+ tasks and 10+ embodiments. We also establish a fair real-robot evaluation platform for externally submitted policies, with standardized tasks, held-out physical objects, controlled initializations, and a unified execution protocol. The platform is publicly accessible at https://ai.midea.com/#/fold-challenge. We hope FolDeX will serve as a unified testbed for heterogeneous real-robot data reuse and reliable long-horizon deformable manipulation.
Abstract screened
arxiv-cs.RO
03Humanoid hands learned three real world tasks from modest data
The recipe transfers three vision-based dexterity tasks to a real humanoid, but reported success is 52.5-80% and still far from general-purpose manipulation.
Full text reviewed
Tesla
Proceedings of Machine Learning Research
04Long VLA kept robots on track through multi step jobs
Long-VLA improves multi-step manipulation by separating movement from interaction inside one model, but dataset labeling is manual and longer sequences still fail.
Full text reviewed
Tesla
Proceedings of Machine Learning Research
05A robot learned to open doors without damaging either side
SafeDiff uses real-time force signals to correct a vision-planned path and transfers with limited real data, but it has only been tested on one constrained task with a gripper.
Full text reviewed
Tesla
Proceedings of Machine Learning Research
01TOP SIGNAL
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Read evidence →RoboBusiness 2026 panel to discuss reshoring with robots
The takeaway: While reshoring manufacturing is a high priority for policymakers and technologists, RoboBusiness panelists will examine how to promote it. The post RoboBusiness 2026 panel to discuss reshoring with robots appeared first on The Robot Report .
02
Timken Precision Drives puts the motion challenge first in humanoid robots
Timken combines the expertise of Cone Drive, SPINEA, CGI Motion, and Timken Encoders to develop application-specific motion systems. The post Timken Precision Drives puts the motion challenge first in humanoid robots appeared first on The Robot Report .
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03NYSOFA expands partnership with Intuition Robotics to bring ElliQ to more seniors
With the expansion, NYSOFA and Intuition Robotics established a new three-year, $2.1 million agreement to keep rolling out ElliQ. The post NYSOFA expands partnership with Intuition Robotics to bring ElliQ to more seniors appeared first on The Robot Report .
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04Q&A: Mars Perseverance Rover Distance Record
On October 11 , the Perseverance rover completed a drive that brought its total distance traveled on Mars to 28.10 miles (45.22 kilometers). This is a new record for Mars rovers, enabled in large part by Perseverance’s ability to drive with much more autonomy than its predecessors. Over the last five years, Perseverance’s autonomy software has been steadily upgraded, and to learn more about how the record was broken and what the future holds for the rover, we spoke with Mark Maimone , the Perseverance Robotic Operations Deputy Team Chief at NASA’s Jet Propulsion Laboratory, who designed and developed the self-driving software for Mars rovers Spirit, Opportunity, Curiosity, and Perseverance. Before we talk about Perseverance, let’s pour one out for the previous record holder Opportunity , w
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05Hello Robot, A&K Robotics to discuss how robots navigate crowded indoor spaces at RoboBusiness
As robots move beyond warehouses and other controlled environments and into everyday life, navigation challenges will be critical. The post Hello Robot, A&K Robotics to discuss how robots navigate crowded indoor spaces at RoboBusiness appeared first on The Robot Report .
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01TOP SIGNAL
Abstract screened
Read evidence →GreenWAM: A World Foundation Model for Robotics
The takeaway: A robot needs a world model that predicts how its actions will change the scene and a policy that chooses those actions. Models are usually built and evaluated for only one of these roles, and every robot records its actions in its own format. We ask whether one robot world model can serve both roles across different bodies, and what makes this work. We present GreenWAM, a world foundation model for robotics, together with the recipe that produces it: (1) a shared anatomical action space that describes 20 robot families, from bimanual arms to dexterous humanoids, in common joint coordinates; (2) joint training on these families in visual, action-conditioned and world-action modes; and (3) policies that split an action expert off the generator. As a world model, GreenWAM attains top-tier trajectory, depth and 3D accuracy on WorldArena 1.0, ahead of every world-action model and video generator in our comparison. As a policy, it scores among the top entries of the RoboTwin 2.0 leaderboard, reaches the highest zero-shot LIBERO-plus success among the compared methods and is competitive with dedicated vision-language-action and world-action policies on CALVIN and LIBERO. We further adapt GreenWAM to a real humanoid, where it succeeds more often than policies adapted from $π_{0.5}$ and from Cosmos 3 Edge, and distill it into a single-step policy for real-time control. Controlled experiments yield three findings: robot specialization improves world prediction and control together; an action expert helps when split off at the start of adaptation; and keeping world-model modes during policy adaptation preserves robustness to scene changes.
02
WAVE-0: Bridging VLA Policies and WAMs with Lightweight Latent Future Prediction
Vision-language-action (VLA) policies excel at generalist robotic manipulation but generate actions without explicitly modeling their environmental consequences. World action models (WAMs) incorporate future prediction, yet most rely on costly video generation, while latent alternatives either use predicted futures only as auxiliary targets or infer latent actions from suboptimal visual transitions through costly large-scale video pretraining. We introduce WAVE-0 (World-model-Augmented VLA Execution), a framework that bridges VLAs and WAMs through a lightweight latent world model. By connecting the semantic understanding of pretrained VLAs with explicit future prediction, WAVE-0 allows anticipated action consequences to directly guide action generation. Built on a pretrained VLA, its 257M world model translates VLM-generated latent action plans into predicted future representations that condition the action expert, unifying latent planning, future prediction, and action generation. The world model is trained directly on ground-truth robot actions with a counterfactual ranking objective, requiring neither pixel reconstruction nor large-scale video pretraining. WAVE-0 achieves success rates of 91.47% on RoboTwin 2.0 and 98.7% on LIBERO, along with 88.0%, 61.4%, and 75.0% on LIBERO-Plus, LIBERO-Pro, and LIBERO-Para, respectively. On real-world tasks, it improves the success rate of $π_{0.5}$ from 38.3% to 73.3%. These results demonstrate strong manipulation performance and generalization across diverse distribution shifts.
Abstract screened
arxiv-cs.RO
03Data-Free Continual Learning for World Action Model with Rectified Generative Replay
World action models (WAMs) built on pretrained video generation models adapt quickly to new robotic tasks, but they forget previously learned tasks under sequential fine-tuning. Experience replay (ER) mitigates such forgetting by rehearsing data of previous tasks, yet this data is often unavailable in robotics, where a policy is deployed as weights only and its training data is not shared along with it. In this work, we ask whether a WAM can achieve data-free continual learning by regenerating its own past. Our key observation is that a WAM can indeed autoregressively regenerate complete rollouts of previously learned tasks and replay them to mitigate forgetting. However, replaying such raw rollouts still lags far behind replaying real data, which we attribute to two issues. First, the WAM is unable to recognize task completion and keeps generating purposeless motion after the task is done. Second, under open-loop generation, the generated actions gradually drift away from the generated observations. To address both issues, we present rectified generative replay (R$^2$-WAM), which truncates generated rollouts at task completion with an agentic evaluator and filters out rollouts whose actions are physically inconsistent with the observations. On the four LIBERO suites and five real-world tasks, R$^2$-WAM reduces forgetting by over 90% relative to sequential fine-tuning and nearly matches ER without accessing any data from previous tasks. Our analysis further reveals that forgetting in WAMs concentrates in inverse dynamics modeling: a forgotten WAM still produces visually plausible observations of previous tasks while its generated actions fail.
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arxiv-cs.RO
04Contractor Filters
We introduce Contractor Filters (CFs), a set-membership architecture for nonlinear continuous-time state estimation with bounded uncertainties and sporadic outliers. CFs represent feasible states symbolically by compositions of contractors rather than rematerializing a geometric enclosure after each update. This provides an abstraction layer between the set-theoretic filtering equations and their numerical realization, so prediction, correction, ambiguity, and outlier handling can be expressed within one algebra without prescribing a state geometry. We derive an exact nominal recursion and a guaranteed robust enclosure, with validated flow inclusions preserving soundness. Optional sub-paving compression then provides finite outer representations. CFs are evaluated against a robust particle filter on range-only and terrain-based localization under outliers, probabilistic model mismatch, association ambiguity, and multimodality. CFs retain the simulated truth in all tested configurations, while particle-filter reliability degrades under persistent bias and spatial aliasing. Contractor strength and compression budget control the trade-off between paving tightness and computational cost, while preserving the enclosure guarantee.
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arxiv-cs.RO
05LADDER: A Long-Horizon Robot Manipulation Benchmark
General-purpose robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks. Existing benchmarks cannot isolate failures because task horizon is either too short or entangled with scene and object variations. We propose LADDER, a long-horizon robot manipulation benchmark scaling from 1 to 10 subgoals within the same environment over three suites (SimplerEnv-Long, RobotArena-$\infty$-Long, and RoboLab-Long), 145 tasks, eight categories, two embodiments, and 2,024 simulated episodes per system. We evaluate two orchestration paradigms: text subgoals for learned low-level policies (VoLo) and tool selection orchestration (CaP-X, GPT-Policy, PSP-Agent, TiPToP). Given the full instruction directly, low-level policies achieve 85.4% on single-subgoal tasks, but drop to near zero on extended tasks. While orchestration remedies this, it remains bounded by physical execution errors of the low-level policy rather than high-level planning and achieves only ~25%. Tool selection orchestration with GPT-Policy on GPT-6 Astra significantly outperforms learned-policy methods, but incurs significant time and cost budget and succeeds only ~62%. Real-world experiments across two embodiments confirm the collapse of the low-level policy and the gain from orchestration. Project website: https://ladder.insait.ai
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arxiv-cs.RO