arXiv Machine Learning
Jun 2
Can Vision Language Models Learn Intuitive Physics from Interaction?
arXiv:2602. 06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world.
Learning concepts with energy functions
We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations.
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OpenAI Blog
May 16, 2017
Robots that learn
We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.
arXiv AI
Jul 3
PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation
arXiv:2607. 01938v1 Announce Type: cross Abstract: Manipulating fast and dynamically moving targets in unstructured 3D environments remains challenging for embodied AI.
arXiv Machine Learning
Aug 12
Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
arXiv:2509. 06191v2 Announce Type: replace-cross Abstract: Recent 3D generative models, which are capable of generating full object shapes from just a few images, now open up new opportunities in robotics.
arXiv AI
Jun 2
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
arXiv:2604. 10579v2 Announce Type: replace-cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.
arXiv AI
Jun 19
Movement Primitives in Robotics: A Comprehensive Survey
arXiv:2601. 02379v2 Announce Type: replace-cross Abstract: Biological systems exhibit a continuous stream of movements, consisting of sequential segments, that allow them to perform complex tasks in a creative and versatile fashion.
Hugging Face Trending Papers
Jun 22
Policy-as-Data: Learning Generalizable HOI Diffusion Models from Simulated Physics
Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current data-driven approaches primarily rely on motion capture datasets, which are expensive to scale and limited in functional diversity.
arXiv AI
Jul 20
SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training
arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.
arXiv AI
Jul 3
BuilderBench: The Building Blocks of Intelligent Agents
arXiv:2510. 06288v4 Announce Type: replace Abstract: Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set by existing data.
arXiv Machine Learning
Jun 11
Fourier Features Let Agents Learn High Precision Policies with Imitation Learning
arXiv:2606. 12334v1 Announce Type: new Abstract: High-precision robotic manipulation requires fine-grained spatial reasoning that is often difficult to achieve with RGB-only policies due to depth ambiguity and perspective scale issues.
Hugging Face Trending Papers
Jul 20
Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.
arXiv Machine Learning
Jun 2
GIFT: Geometry-Induced Functional Transfer for Category-level Object Manipulation
arXiv:2503. 15371v2 Announce Type: replace-cross Abstract: Robotic manipulation of unfamiliar objects in new environments is challenging due to limited generalisation capabilities.