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Works68 from public data
- 1,232
- Foundation models in robotics: Applications, challenges, and the future459
How foundation models contribute to improving robot capabilities in the domains of perception, decision-making, and control is explored and the challenges hindering the adoption of foundation models in robot autonomy are discussed.
- MimicPlay: Long-Horizon Imitation Learning by Watching Human Play391
A hierarchical learning framework named MimicPlay is introduced that learns latent plans from human play data to guide low-level visuomotor control trained on a small number of teleoperated demonstrations and outperforms state-of-the-art imitation learning methods in task success rate, generalization ability, and robustness to disturbances.
- Cross-View Semantic Segmentation for Sensing Surroundings336
A novel visual task called Cross-view Semantic Segmentation as well as a framework named View Parsing Network (VPN) to address it and the experimental results show that the model can effectively make use of the information from different views and multi-modalities to understanding spatial information.
- Large AI Models in Health Informatics: Applications, Challenges, and the Future233
Seven key sectors in which large AI models are applicable and might have substantial influence, including: 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics are identified.
- BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation197
BEHAVIOR-1K's human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research, and it is hoped that its human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research.
- 176
- A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook142
This article introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks, and delves into the potential future directions behind the emergence of reasoning abilities within foundation models.
- 100
- Dojo: A Differentiable Physics Engine for Robotics84
A differentiable rigid-body-dynamics simulator for robotics that prioritizes physical accuracy and differentiability is presented and the usefulness of the simulator and its gradients is demonstrated through a number of examples including: simulation, trajectory optimization, reinforcement learning, and system identification.
- Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model81
This paper quantifies the uncertainty of diffusion dynamics models using Conformal Prediction (CP), inspired by concepts from conformal prediction, and modify the loss function for training the diffusion model to include a quantile term to encourage more robust performance across the variety of training examples.
- A Survey of Reasoning with Foundation Models73
This paper introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks and discusses the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning.
- PlaTe: Visually-Grounded Planning With Transformers in Procedural Tasks69
This work addresses the problem of how to leverage instructional videos to facilitate the understanding of human decision-making processes, focusing on training a model with the ability to plan a goal-directed procedure from real-world videos with Planning Transformer (PlaTe), which has the advantage of circumventing the compounding prediction errors that occur with single-step models during long model-based rollouts.
- SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud65
A semantic context encoder is proposed to leverage the free-of-charge semantic segmentation masks in the bird's eye view to address the semantic context information in LiDAR.
- 37
- ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces36
ClawsBench, a benchmark for evaluating and improving LLM agents in realistic productivity settings, is introduced and eight recurring patterns of unsafe behavior are identified, including multi-step sandbox escalation and silent contract modification.
- Lyra: Orchestrating Dual Correction in Automated Theorem Proving35
The Lyra is introduced, a new framework that employs two distinct correction mechanisms: Tool Correction (TC) and Conjecture Correction (CC), an error feedback mechanism designed to interact with prover to refine formal proof conjectures with prover error messages.
- ParticleFormer: A 3D Point Cloud World Model for Multi-Object, Multi-Material Robotic Manipulation34
This work presents ParticleFormer, a Transformer-based point cloud world model trained with a hybrid point cloud reconstruction loss, supervising both global and local dynamics features in multi-material, multi-object robot interactions, and extends existing dynamics learning benchmarks to include diverse multi-material, multi-object interaction scenarios.
- Egocentric Human Trajectory Forecasting With a Wearable Camera and Multi-Modal Fusion29
A Transformer-based encoder-decoder neural network model, integrated with a novel cascaded cross-attention mechanism that fuses multiple modalities, has been designed to predict the future trajectory of the camera wearer, with results showing that the model outperforms the state-of-the-art methods in egocentric human trajectory forecasting.
- ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly27
This work proposes a hierarchical modular approach, named Adaptive Robotic Compositional Hierarchy (ARCH), which enables long-horizon, high-precision robotic assembly in contact-rich settings and shows that ARCH generalizes well to unseen objects and outperforms baseline methods in terms of success rate and data efficiency.
- GRAD-NAV++: Vision-Language Model Enabled Visual Drone Nav igation With G aussian Ra diance Fields and D ifferentiable Dynamics26
This work introduces a lightweight Vision–Language–Action (VLA) framework that runs fully onboard and follows natural-language commands in real time and establishes a new benchmark for fully onboard Vision-Language-Action (VLA) flight and demonstrates that compact, efficient models can enable reliable, language-guided navigation without relying on external infrastructure.
- Transferable Active Grasping and Real Embodied Dataset23
A novel mask-guided reward is proposed to overcome the sparse reward issue in grasping and ensure category-irrelevant behavior in grasping in a real-world UR-5 robotic arm.
- Leveraging Print Debugging to Improve Code Generation in Large Language Models22
This work proposes an in-context learning approach that guides LLMs to debug by using a "print debugging" method, which involves inserting print statements to trace and analysing logs for fixing the bug.
- 22
- Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition17
The design and outcomes of the End-to-End Autonomous Driving through V2X Cooperation Challenge are described, key research problems including bandwidth-aware fusion, robust multi-agent planning, and heterogeneous sensor integration are highlighted, and emerging technical trends among top-performing solutions are analyzed.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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