Trajectory Planning for Humanoid Robots and Inverse Trajectory Planning for Service Robots: A Bridge Between Robots and Humans

Pedro Antonio Pena

Soon, there will be robots that will live in our homes. The types of robots in our homes will not only be vacuum robots that are small and non-intrusive, but there will also be general-purpose robots that will be able to help us cook, do our laundry, and move around our homes in ways that resemble humans. Never in history have we ever had such agents, and they might impose danger if we do not carefully consider their behavior and motion planners. For a robot to move in our space, it needs to not only know how to avoid obstacles but also infer the position of a human in the environment. Furthermore, there needs to be an interface that allows robots and humans to communicate to understand what a robot is planning as well as for a robot to know what a human desires. Therefore we propose a vision JAMP, Joint Autonomy in Motion Planning, that consists of four components: Trajectory Planning, Inverse Trajectory Planning, Human-Robot Interface, and Robot Expressions, to produce a behavior that will consider the human’s desires as well as motions that take into account future human states to convey intention and safety to other humans. In my research, I worked on different components of JAMP individually to tackle different unsolved problems. I made contributions to trajectory planning for humanoid robots that can generate coupled motions while maintaining the robot stable. The trajectory planner was applied to soccer humanoid robots for kicking a ball while the robot is walking. The adaptive walk-kick trajectory controller for humanoid robots allows humanoids to kick in any direction while it is walking and was verified on an NAO robot; the stability and reliability of each kick have been evaluated 30 times for each kick motion trajectory while performing demanding motions. I also introduce a trajectory planner that considers humans in the environment in which a robot follows a human based on a nonlinear optimization to infer the 3D pose of a human wearing an infrared transmitter. Inverse trajectory planning allows a robot to infer future plausible states of a human, so I propose a novel probabilistic framework for robotic systems in which multiple models can be fused into a circular probabilitymap to forecast human poses, allowing models from different applications to integrate into one representation that can be used by the robot. We tested the framework on Toyota’s HSR robot and Waymo Open Dataset. Moreover, a human-robot interface that allows a human and robot to communicate is also considered in this research in which an augmented reality-based human-robot interface that can be used to place and navigate a robot and map and label an environment will be introduced, as well as a human-robot interface that integrates multimodal communication features of a 3-dimensional graphical social virtual agent with a high degree of freedom service robot, HSR. We demonstrate HSR greeting gestures using culturally diverse inspired motions, combined with our virtual social agent interface, and we provide the results of a pilot study designed to assess the effects of our multimodal virtual agent/robot system on users’ experience.For robot expressions, I discuss philosophical remarks of how robots in the future will move and interact in the presence of humans and other robots and how they affect other agents in the environment. We think about how we can evaluate the effectiveness of expressions in agents and introduce a metric, Pena coefficient, that measures the potential of agent expression based on DoR, degrees of reality. Moreover, we revisit social agents and inspect how social actions can be seen from the perspective of degrees of freedom and present ASCEE and SDoF that can be used to measure social degrees of freedom in agents, a concept never visited as of yet but is important to think about when evaluating agents in the environment of humans. We introduce human-robot laws that describe different types of interactions that will be deemed necessary in the future for human-robot interaction and present different levels of interactions between humans and robots and robots and robots. As we think about the interactions between these agents, we introduce a paradox, we call Robot Expression Paradox, which is introduced when robot-robot and human-robot expressions affect each other. To potentially resolve this paradox, we introduce REN, a robot expression network, that can be used to propagate changes among these different interactions. </div></div></div>

Trajectory Planning for Humanoid Robots and Inverse Trajectory Planning for Service Robots: A Bridge Between Robots and Humans

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