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Nov 11Wed
  1. The Decoder72

    Nvidia's model GR00T 2 watches humans on video, then imagines itself as the robot

    Nvidia's GR00T 2 is a "world-action model" that predicts both the next movement and a short video of the near future, letting it transfer human movement sequences from video to different robot form factors instead of relying on teleoperation. Trained on over a thousand GB300 chips with about 400,000 hours of head-mounted-camera footage, it learned tasks such as lighting a match or slicing a banana from human demonstrations, with success rates between 10 and 80 percent, and a higher-level language model raised toy-car assembly success to at least 97 percent. Nvidia notes hurdles including no direct touch sensor data, missed subtle errors, and idle time of roughly a third during trash sorting while waiting on the planning model.

Nov 10Tue
  1. Hacker News AI71

    AI agents use simulation to solve tough robotics problems with code, not data

    At the 2026 Conference on Robot Learning, UC Berkeley and NVIDIA announced "agentic robotics," an approach in which LLM-based coding agents decompose tasks like grocery packing into sub-tasks, write and iterate code in simulation, and even tune simulation parameters to match real-world video. UC Berkeley testing reached success rates up to 99 percent, matching human hand-engineered performance, versus 20 percent for baseline imitation learning, and NVIDIA's Spencer Huang said retasking a robot at Wistron drops from at least 8 hours to a minute or two. Intrinsic's Torsten Kroeger cautioned that no physics engine accurately models physical contact and raised alignment concerns about trusting agents.

Nov 9Mon
  1. MIT News AI38

    AI-assisted learning platform helps robots reason beyond literal commands

    MIT and NYU researchers introduced FLEX, a flexible feature-editing pipeline for robot motion-planning that lets robots interpret natural-language commands and generalize beyond their literal meaning. FLEX uses a three-step process — Add, Generalize (via the LLM GPT-4o), and Mask — so a robot told "Don't spill on the laptop!" also avoids other electronics, and it was demonstrated in simulations of serving coffee, moving a candle, and carrying a bowling ball. The study, led by MIT assistant professor Andreea Bobu, will be presented at the annual Conference on Robot Learning.

Nov 7Sat
  1. Hacker News AI58

    VFlash: Enabling System-1 VLMs for Physical AI

    VFlash is a generalist decoding framework for Physical AI that adapts VLM speculative decoding by exploiting the known structure of visual primitives, using a lightweight drafter trained on annotated geometry to generate complete primitives in one parallel pass while the target VLM stays frozen. It reduces mean 3D displacement error by 42.6% relative to the autoregressive target across three robot datasets and three target variants, and reaches 70.2% average success on MolmoSpaces versus 62.6% for the baseline. On Jetson Thor, DGX Spark, and H100, VFlash cuts decoding latency by 12–42×, yielding 5× faster robot inference and over 3× faster NAVSIM driving inference on Jetson Thor and DGX Spark.

Nov 6Fri
  1. Allen Institute for AI Blog55

    ToyBlocks: A controlled world for robot learning and evaluation

    Ai2 introduces FoundationWorlds, a framework pairing controlled simulated environments with tools for automatically generating tasks, demonstrations, and behavioral metrics, starting with ToyBlocks, an open simulation of a robot arm manipulating blocks on a tabletop. ToyBlocks generates demonstrations via reinforcement learning, motion planning, and a task-and-motion planner at up to 24 times real-time on a single GPU, and supports studies of data scaling, spatial generalization, long-horizon planning, world models, and sim-to-real transfer. The initial release includes 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames, plus two trained models, training code, and evaluation code and validation sets.

  2. Hugging Face Blog58

    ToyBlocks: A controlled world for robot learning and evaluation

    ToyBlocks is an open simulation framework built around a robot arm and blocks on a tabletop that automatically generates tasks and demonstrations, using reinforcement learning and motion planning to produce skills such as pushing, flipping, and stacking. It supports studies of low-level action model generalization, high-level planning with vision-language models, agentic systems using tool calls, action-conditioned world models, and zero-shot sim-to-real transfer, with an initial release of 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames. The authors present ToyBlocks as the first of their FoundationWorlds and plan a second release with a larger dataset, more skills, and integrated RL training.

  3. MIT News AI34

    Clearing up a robot’s fuzzy memory

    MIT researchers developed UQ-DAAAM, a memory system that quantifies a robot's uncertainty about object descriptions by comparing multiple viewpoints and fusing them into more accurate captions, resolving ambiguities for about 92 percent of objects with only two additional captions each. The system improved object description accuracy by 5 percent over the state of the art and produced more accurate answers about objects, their locations, and when they were seen. The work, led by senior author Luca Carlone with co-lead authors Harry Zhang and Nicolas Gorlo, will be presented at the Conference on Neural Information Processing Systems.

  4. Hacker News AI40

    AI e-nose and wearable device deliver programmable smells with 73% accuracy

    Researchers at The Ohio State University developed an AI e-nose paired with a wearable scent-release system called Scentronic, which uses machine learning to classify odor data in real time. Trained on five food scents (coffee, beer, red wine, orange and chocolate cake), the system let participants identify individual odors about 73% of the time and paired scents about 71% of the time. The prototype was built in wristband and necklace versions, and the team's next steps include testing integration into watches and smart jewelry.

Nov 4Wed
  1. Hacker News AI31

    EdgeDAE: Acceleration of Diffusion Action Experts for Real-Time Physical AI

    EdgeDAE is a heterogeneous FPGA-GPU system that offloads the perception-heavy vision transformer to the GPU while accelerating Diffusion Action Expert (DAE) inference on an FPGA by storing all parameters on-chip in BRAM/URAM, eliminating the repeated DRAM parameter loading of iterative denoising. It co-designs quantization, fixed-point arithmetic, and hardware-efficient random number generation for the FPGA fabric. Compared with an edge GPU baseline, it reduces end-to-end inference latency by 52.5% for Octo-Small and 38.7% for Octo-Base with up to 2.10x higher throughput, and achieves about 17x higher energy efficiency than an RTX 4090.

  2. Hacker News AI58

    Researchers warn secret "trigger words" can make hacked AI robots attack people

    Researchers at Binghamton University demonstrated an attack method called MuTRAP that plants hidden instructions in large language models, activating when a specific trigger word appears. In an experiment, a Segway-based robot programmed to slice an apple instead targeted a model human hand after the trigger "CF" was added to the command; the hand was plastic and the cutting simulated. The team also developed a potential solution called PROTEA, which analyzes a robot's planned actions and checks whether they could cause harm.