arXiv Machine Learning
Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics
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arXiv Machine Learning
Jun 15
PhysVLA: Towards Physically-Grounded VLA for Embodied Robotic Manipulation
arXiv:2606. 13886v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models excel at mapping visual inputs and natural language instructions directly to robotic control policies.
arXiv AI
Jul 9
Rapidly Learning Soft Robot Control via Implicit Time-Stepping
arXiv:2511. 06667v2 Announce Type: replace-cross Abstract: With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies.
arXiv Machine Learning
Jun 25
TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU
arXiv:2606. 24039v1 Announce Type: cross Abstract: Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference.
Hugging Face Trending Papers
Aug 9
Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation
Differentiable simulation is a key component in learning, control, and inverse problems, where gradients through nonlinear implicit solvers are required. Existing approaches either rely on unrolled automatic differentiation, whose memory grows with solver depth, or on equation-level implicit differentiation, which assembles global Jacobians and solves large sparse adjoint systems, discarding the locality of the forward solver -- and differentiating the converged equation rather than the finite computation that actually ran.
arXiv Machine Learning
Aug 11
Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation
arXiv:2608. 08559v1 Announce Type: cross Abstract: Differentiable simulation is a key component in learning, control, and inverse problems, where gradients through nonlinear implicit solvers are required.
Hugging Face Trending Papers
Sep 10
2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation
The paper introduces 2AM, a system that keeps task memory solely within a multimodal Agent while using a single RGB‑based, stateless Action Model to execute motions. By compiling interaction history into subtask language and optional 2D grasp/place/move hints, the Agent steers the Action Model, which is trained to tolerate imperfect guidance through dropout, noise, and jitter. On the LIBERO‑Mem benchmark, 2AM achieves 76.3% average completion without depth, geometry, or planners, vastly outperforming the best baseline.