Chuang Gan

Ziyan Xiong, Lixing Fang, Junyun Huang et al. · cmu

Building a low-latency humanoid teleoperation system is essential for collecting diverse reactive and dynamic demonstrations. However, existing approaches rely on heavily pre-processed human-to-humanoid motion retargeting and position-only PD control, resulting in substantial latency that severely limits responsiveness and prevents tasks requiring rapid feedback and fast reactions. To address this problem, we propose ExtremControl, a low latency whole-body control framework that: (1) operates directly on SE(3) poses of selected rigid links, primarily humanoid extremities, to avoid full-body retargeting; (2) utilizes a Cartesian-space mapping to directly convert human motion to humanoid link targets; and (3) incorporates velocity feedforward control at low level to support highly responsive behavior under rapidly changing control interfaces. We further provide a unified theoretical formulation of ExtremControl and systematically validate its effectiveness through experiments in both simulation and real-world environments. Building on ExtremControl, we implement a low-latency humanoid teleoperation system that supports both optical motion capture and VR-based motion tracking, achieving end-to-end latency as low as 50ms and enabling highly responsive behaviors such as ping-pong ball balancing, juggling, and real-time return, thereby substantially surpassing the 200ms latency limit observed in prior work.

Yian Wang, Han Yang, Minghao Guo et al.

Automatically generating interactive 3D environments is crucial for scaling up robotic data collection in simulation. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between objects (e.g., contact, support, balance, and containment), which are essential for creating complex and realistic manipulation scenarios such as tabletop arrangements, shelf organization, or box packing. Compared to classical 3D layout generation, producing complex physical scenes introduces additional challenges: (a) higher object density and complexity (e.g., a small shelf may hold dozens of books), (b) richer supporting relationships and compact spatial layouts, and (c) the need to accurately model both spatial placement and physical properties. To address these challenges, we propose PhyScensis, an LLM agent-based framework powered by a physics engine, to produce physically plausible scene configurations with high complexity. Specifically, our framework consists of three main components: an LLM agent iteratively proposes assets with spatial and physical predicates; a solver, equipped with a physics engine, realizes these predicates into a 3D scene; and feedback from the solver informs the agent to refine and enrich the configuration. Moreover, our framework preserves strong controllability over fine-grained textual descriptions and numerical parameters (e.g., relative positions, scene stability), enabled through probabilistic programming for stability and a complementary heuristic that jointly regulates stability and spatial relations. Experimental results show that our method outperforms prior approaches in scene complexity, visual quality, and physical accuracy, offering a unified pipeline for generating complex physical scene layouts for robotic manipulation.