Geometric Action Model (GAM) repurposes geometric foundation models for 3D-aware robot manipulation
Researchers propose the Geometric Action Model (GAM), a language-conditioned robot manipulation policy that splits a pretrained geometric foundation model (GFM) to serve simultaneously as an observation encoder, causal future predictor, and action decoder. Unlike existing vision-language-action models that operate on 2D image frames, GAM explicitly incorporates 3D geometric priors for contact-rich manipulation. The approach claims improvements in accuracy, robustness, speed, and model size over foundation-model-scale baselines across simulation and real-robot benchmarks.
Related guides (2)
Related events (8)
DynaFLIP: Dynamics-Aware Multimodal Pre-Training for Robot Manipulation Perception
DynaFLIP is a pre-training framework that injects motion understanding into visual encoders for robot manipulation by constructing image-language-3D flow triplets from human and robot videos. The method encourages tri-modal alignment via simplex-volume minimization in a shared hyperspherical space, combined with cosine regularization and contrastive objectives. The resulting dynamics-aware visual backbone consistently outperforms baselines across diverse downstream policies including VLAs, with gains up to +22.5% in out-of-distribution scenarios. The work argues that robot generalization requires encoding how the world changes under action, not just static scene content.
π0 and π0-FAST: Vision-Language-Action Models for General Robot Control
Hugging Face published a blog post covering π0 and π0-FAST, vision-language-action (VLA) models developed for general-purpose robot control. These models combine vision and language understanding with action generation to enable robots to perform a broad range of manipulation tasks. The post appears to be a technical overview or release commentary on Physical Intelligence's robotics foundation models, situating them within the broader VLA research landscape.
AHA-WAM: Asynchronous world-action modeling with temporal decoupling for robot manipulation
AHA-WAM introduces a dual Diffusion Transformer architecture that decouples world prediction (low-frequency) from action execution (high-frequency) in robot manipulation policies, addressing the inefficiency of existing world-action models that force both branches to operate at the same temporal resolution. The system uses a rolling key-value memory video DiT as a long-horizon scene planner and a fast action DiT that queries layerwise latent context via joint attention, with Observation-Guided Video-Context Routing enabling asynchronous execution. On RoboTwin benchmarks, AHA-WAM achieves 92.80% average success and 78.3% on real-world tasks at 24.17 Hz, a 4.59x speedup over Fast-WAM, without robot-data pretraining.
GRASP: Gradient-based Planning for World Models at Longer Horizons
Researchers from Berkeley, Meta, and collaborators introduce GRASP, a gradient-based planner designed to make long-horizon planning with learned world models more robust. The method addresses three core failure modes: ill-conditioned computation graphs from backpropagation through time, non-greedy loss landscapes with many local minima, and brittle gradients through high-dimensional vision models. GRASP lifts trajectory optimization into virtual states for parallel optimization across time, injects stochasticity into state iterates for exploration, and reshapes gradients to avoid problematic state-input gradient paths. The work is positioned in the context of scaling world models toward general-purpose simulators usable for control and planning.
LabVLA: Vision-Language-Action model and RoboGenesis data engine for scientific laboratory robotics
Researchers introduce LabVLA, a Vision-Language-Action model designed to bridge written scientific protocols and physical robot execution in laboratory settings. To address the data scarcity problem, they build RoboGenesis, a simulation-based data engine that composes lab workflows from atomic skills and generates structured demonstrations across robot embodiments. LabVLA uses a two-stage training recipe combining FAST action token pretraining on a Qwen3-VL-4B-Instruct backbone with flow matching posttraining via a DiT action expert. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among evaluated baselines in both in-distribution and out-of-distribution settings.
Mana framework achieves zero-shot sim-to-real transfer for dexterous articulated tool manipulation
Researchers introduce Mana (Manipulation Animator), a sim-to-real framework that reframes dexterous robotic manipulation as an animation problem using a coarse-to-fine pipeline of procedurally-generated grasp keyframes, motion planning, and reinforcement learning. The system requires minimal human input (under one minute per tool) and achieves zero-shot sim-to-real transfer across four articulated tools with varying joint types and scales. The work addresses a longstanding gap in dexterous robotics where articulated tool use—requiring coordination of internal degrees of freedom and contact-rich interactions—has been underexplored relative to rigid object manipulation.
AnyMo: Geometry-Aware Setup-Agnostic Framework for Wearable IMU Human Motion Understanding
AnyMo is a geometry-aware framework that addresses the setup-dependence problem in wearable IMU-based human motion modeling by using physics-grounded simulation over dense body-surface placements to generate synthetic training signals. It pre-trains a graph encoder from synthetic placement views and masked partial observations, then tokenizes multi-position IMU data into full-body motion tokens aligned with an LLM for motion-language understanding. Evaluated across zero-shot activity recognition (14 unseen datasets), cross-modal retrieval, and motion captioning, AnyMo improves average Accuracy/F1 by ~11.7%/11.6%, zero-shot retrieval MRR by 15.9–28.6%, and captioning BERT-F1 by 18.8%. The work positions itself as a generalist model for wearable motion understanding transferable across devices and sensing configurations.
FM-CGM: Foundation Model Framework for Zero-Shot Visual Causal Generative Modeling
FM-CGM is a modular framework that decomposes visual causal reasoning into three components—concept extractor, concept manipulator, and counterfactual generator—using pretrained foundation models without task-specific causal training. The approach combines a large reasoning model for causal inference with a text-to-image diffusion model for generation, enabling zero-shot causal discovery and counterfactual image synthesis. A novel cross-attention mechanism called Causal Semantic Guidance (CSG) ensures that semantic interventions propagate correctly through causal descendants while preserving unaffected image regions. Empirical results show the framework can identify plausible causal structures and generate faithful counterfactual images.