This paper addresses the critical need for refining robot motions that,
despite achieving a high visual similarity through human-to-humanoid
retargeting methods, fall short of practical execution in the physical realm.
Existing techniques in the graphics community often prioritize visual fidelity
over physics-based feasibility, posing a significant challenge for deploying
bipedal systems in practical applications. Our research introduces a
constrained reinforcement learning algorithm to produce physics-based
high-quality motion imitation onto legged humanoid robots that enhance motion
resemblance while successfully following the reference human trajectory. We
name our framework: I-CTRL. By reformulating the motion imitation problem as a
constrained refinement over non-physics-based retargeted motions, our framework
excels in motion imitation with simple and unique rewards that generalize
across four robots. Moreover, our framework can follow large-scale motion
datasets with a unique RL agent. The proposed approach signifies a crucial step
forward in advancing the control of bipedal robots, emphasizing the importance
of aligning visual and physical realism for successful motion imitation.

本文介绍了一种通过有约束的强化学习算法实现对双足机器人进行基于物理的高质量动作模仿的框架，强调将视觉和物理现实性相结合对于成功实现动作模仿的重要性。

I-CTRL：基于约束强化学习的仿真控制人形机器人

I-CTRL: Imitation to Control Humanoid Robots Through Constrained  Reinforcement Learning

In this paper we propose a new flexible method for hand-eye calibration. The
vast majority of existing hand-eye calibration techniques requires a
calibration rig which is used in conjunction with camera pose estimation
methods. Instead, we combine structure-from-motion with known robot motions and
we show that the solution can be obtained in linear form. The latter solves for
both the hand-eye parameters and for the unknown scale factor inherent with
structure-from-motion methods. The algebraic analysis that is made possible
with such a linear formulation allows to investigate not only the well known
case of general screw motions but also such singular motions as pure
translations, pure rotations, and planar motions. In essence, the robot-mounted
camera looks to an unknown rigid layout, tracks points over an image sequence
and estimates the camera-to-robot relationship. Such a self calibration process
is relevant for unmanned vehicles, robots working in remote places, and so
forth. We conduct a large number of experiments which validate the quality of
the method by comparing it with existing ones.

本文提出了一种新的灵活的手眼标定方法，结合运动估计技术和已知机器人运动，通过线性形式求解手眼参数和结构运动方法中的未知比例因子。该方法适用于无人驾驶车辆、远程工作机器人等领域，并通过与现有方法的对比实验验证了其优越性。

机器人手眼标定：基于运动结构的方法

Robot Hand-Eye Calibration using Structure-from-Motion

Planning safe robot motions in the presence of humans requires reliable
forecasts of future human motion. However, simply predicting the most likely
motion from prior interactions does not guarantee safety. Such forecasts fail
to model the long tail of possible events, which are rarely observed in limited
datasets. On the other hand, planning for worst-case motions leads to overtly
conservative behavior and a ``frozen robot''. Instead, we aim to learn
forecasts that predict counterfactuals that humans guard against. We propose a
novel game-theoretic framework for joint planning and forecasting with the
payoff being the performance of the planner against the demonstrator, and
present practical algorithms to train models in an end-to-end fashion. We
demonstrate that our proposed algorithm results in safer plans in a crowd
navigation simulator and real-world datasets of pedestrian motion. We release
our code at
this https URL

通过学习人类常常防范的对策，我们提出一种新颖的博弈论框架，用于联合规划和预测，以提高机器人在人群导航中的安全性。