Robots that interact with humans in a physical space or application need to
think about the person's posture, which typically comes from visual sensors
like cameras and infra-red. Artificial intelligence and machine learning
algorithms use information from these sensors either directly or after some
level of symbolic abstraction, and the latter usually partitions the range of
observed values to discretize the continuous signal data. Although these
representations have been effective in a variety of algorithms with respect to
accuracy and task completion, the underlying models are rarely interpretable,
which also makes their outputs more difficult to explain to people who request
them. Instead of focusing on the possible sensor values that are familiar to a
machine, we introduce a qualitative spatial reasoning approach that describes
the human posture in terms that are more familiar to people. This paper
explores the derivation of our symbolic representation at two levels of detail
and its preliminary use as features for interpretable activity recognition.

介绍了一种基于人类肢体空间关系推理的符号表示方法，用于解释机器人看到的人体姿态，实现可解释的活动识别。

用符号表示人体姿势的解释性学习与推理

A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning

When robots interact with human partners, often these partners change their
behavior in response to the robot. On the one hand this is challenging because
the robot must learn to coordinate with a dynamic partner. But on the other
hand -- if the robot understands these dynamics -- it can harness its own
behavior, influence the human, and guide the team towards effective
collaboration. Prior research enables robots to learn to influence other robots
or simulated agents. In this paper we extend these learning approaches to now
influence humans. What makes humans especially hard to influence is that -- not
only do humans react to the robot -- but the way a single user reacts to the
robot may change over time, and different humans will respond to the same robot
behavior in different ways. We therefore propose a robust approach that learns
to influence changing partner dynamics. Our method first trains with a set of
partners across repeated interactions, and learns to predict the current
partner's behavior based on the previous states, actions, and rewards. Next, we
rapidly adapt to new partners by sampling trajectories the robot learned with
the original partners, and then leveraging those existing behaviors to
influence the new partner dynamics. We compare our resulting algorithm to
state-of-the-art baselines across simulated environments and a user study where
the robot and participants collaborate to build towers. We find that our
approach outperforms the alternatives, even when the partner follows new or
unexpected dynamics. Videos of the user study are available here:
this https URL

该研究探讨机器人与人类的互动，提出了一种基于机器学习的方法，能够训练机器人逐渐适应人体动态的变化，并使机器人与人类在协作过程中达到更好的效果。

RILI: 鲁棒地影响潜在意图

RILI: Robustly Influencing Latent Intent

Moving a human body or a large and bulky object can require the strength of
whole arm manipulation (WAM). This type of manipulation places the load on the
robot's arms and relies on global properties of the interaction to
succeed---rather than local contacts such as grasping or non-prehensile
pushing. In this paper, we learn to generate motions that enable WAM for
holding and transporting of humans in certain rescue or patient care scenarios.
We model the task as a reinforcement learning problem in order to provide a
behavior that can directly respond to external perturbation and human motion.
For this, we represent global properties of the robot-human interaction with
topology-based coordinates that are computed from arm and torso positions.
These coordinates also allow transferring the learned policy to other body
shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue
scenario and show in quantitative experiments that the policy can solve unseen
scenarios with differently-shaped humans, floating humans, or with perception
noise. Our qualitative experiments show the subsequent transporting after
holding is achieved and we demonstrate that the policy can be directly
transferred to a real world setting.

本文利用基于拓扑的坐标将任务建模为强化学习问题，以直接响应外部干扰和人体动作的行为方式，学习生成运动，解决某些救援或病人护理场景中的大型物品运输。仿真动态海上救援场景并进行定量实验，展示学习策略可以解决不同形状的人类，漂浮的人类或感知噪声。我们的定性实验展示了持续保持后的运输，证明了该策略可以直接转移到实际场景中。

基于拓扑表示的强化学习在带整臂操作的人体运动中的应用

Reinforcement Learning in Topology-based Representation for Human Body  Movement with Whole Arm Manipulation

Object handover is a basic, but essential capability for robots interacting
with humans in many applications, e.g., caring for the elderly and assisting
workers in manufacturing workshops. It appears deceptively simple, as humans
perform object handover almost flawlessly. The success of humans, however,
belies the complexity of object handover as collaborative physical interaction
between two agents with limited communication. This paper presents a learning
algorithm for dynamic object handover, for example, when a robot hands over
water bottles to marathon runners passing by the water station. We formulate
the problem as contextual policy search, in which the robot learns object
handover by interacting with the human. A key challenge here is to learn the
latent reward of the handover task under noisy human feedback. Preliminary
experiments show that the robot learns to hand over a water bottle naturally
and that it adapts to the dynamics of human motion. One challenge for the
future is to combine the model-free learning algorithm with a model-based
planning approach and enable the robot to adapt over human preferences and
object characteristics, such as shape, weight, and surface texture.

本文介绍了一种基于学习算法的动态物体移交方法，通过与人类交互学习物体移交的潜在报酬，使机器人能够自然地适应人体运动的动态，并通过上下文策略搜索来建立问题模型。