Physics simulation environments and sim-to-real transfer techniques.
Yufei Jia, Zhanxiang Cao, Mingrui Yu et al.
Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-centric execution path
Christen Millerdurai, Shaoxiang Wang, Yaxu Xie et al.
Reconstructing the absolute 3D pose and shape of the hands from the user's viewpoint using a single head-mounted camera is crucial for practical egocentric interaction in AR/VR, telepresence, and hand-centric manipulation tasks, where sensing must remain compact and unobtrusive.
Chi Zhang, Penglin Cai, Ziheng Xi et al.
TTP transfers tactile experience from human demonstrations into robot policies for contact-sensitive manipulation.
Zhihao Cao, Qi Shao, Shuhao Zhai et al.
Collaborative photorealistic 3D reconstruction from multiple agents enables rapid large-scale scene capture for virtual production and cooperative multi-robot exploration.
Marco Coscoy, Zewei Zhou, Seth Z. Zhao et al.
Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and negotiate with each other to benefit the final planning.
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Jiye Lee, Yonghun Choi, Jungdam Won
Two-stage framework that enhances noisy multi-human object interaction (MHOI) data by generating plausible hand grasps and refining full-body motion via diffusion-based optimization.
Jiaxu Wang, Junhao He, Jingkai Sun et al.
MoSA closes the real-to-sim gap for continuum robots by learning a residual stress field that captures anisotropic material behavior missed by standard simulators.
Chengyue Huang, Khang Vo Huynh, Sebastian Elbaum et al.
Robotic manipulation is typically evaluated by task success, but successful completion does not guarantee safe execution. Many rl failures are temporal: a robot may touch a clean surface after contamination or release an object before it is fully inside an enclosure.
Zachary Kousik, Shanpu Fang, Rohan Thakker et al.
An open-source 3D-printed humanoid robot platform designed for reinforcement-learning based locomotion research with affordable hardware.
Wang J., Xiao B., Wang J., Heider P., Li H., Zhang Y.
An Isaac-Gym-based training framework for humanoid locomotion that enables zero-shot sim-to-real transfer on XBot-L/XBot-S robots via domain randomization.
Nikita Rudin, David Hoeller, Philipp Reist et al.
Legged Gym is an open-source training framework built on NVIDIA Isaac Gym that enables massively parallel deep RL for legged robot locomotion, achieving policy training times of minutes rather than days.
Nikita Rudin, David Hoeller, Marco Hutter
RSL-RL is a lightweight, high-performance PyTorch library implementing PPO tailored for locomotion. Used as the default RL backend in Legged Gym, it enables training thousands of agents in parallel on GPU.
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo et al.
A GPU-accelerated physics simulation framework that enables thousands of RL environments to run in parallel on a single GPU, eliminating CPU-GPU transfer bottlenecks.
Josh Tobin, Rachel Fong, Alex Ray et al.
Introduced domain randomization for vision-based manipulation: randomize simulation textures, lighting, and camera parameters so that the real world looks like just another variation.
Read these papers in order to build expertise in Simulation.
Yufei Jia, Zhanxiang Cao et al. · 2026
Christen Millerdurai, Shaoxiang Wang et al. · 2026
Chi Zhang, Penglin Cai et al. · 2026
Zhihao Cao, Qi Shao et al. · 2026
Marco Coscoy, Zewei Zhou et al. · 2026
Jiye Lee, Yonghun Choi et al. · 2026
…and 8 more papers.
