Mechanical engineering challenges in humanoid robotics

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 36-39).Humanoid robots are artificial constructs designed to emulate the human body in form and function. They are a unique class of robots whose anthropomorphic nature renders them particularly well-suited to interact with humans in a world designed for humans. The present work examines a subset of the plethora of engineering challenges that face modem developers of humanoid robots, with a focus on challenges that fall within the domain of mechanical engineering. The challenge of emulating human bipedal locomotion on a robotic platform is reviewed in the context of the evolutionary origins of human bipedalism and the biomechanics of walking and running. Precise joint angle control bipedal robots and passive-dynamic walkers, the two most prominent classes of modem bipedal robots, are found to have their own strengths and shortcomings. An integration of the strengths from both classes is likely to characterize the next generation of humanoid robots. The challenge of replicating human arm and hand dexterity with a robotic system is reviewed in the context of the evolutionary origins and kinematic structure of human forelimbs. Form-focused design and function-focused design, two distinct approaches to the design of modem robotic arms and hands, are found to have their own strengths and shortcomings. An integration of the strengths from both approaches is likely to characterize the next generation of humanoid robots.by Peter Guang Yi Lu.S.B

Sequential Motion Planning for Bipedal Somersault via Flywheel SLIP and Momentum Transmission with Task Space Control

In this paper, we present a sequential motion planning and control method for generating somersaults on bipedal robots. The somersault (backflip or frontflip) is considered as a coupling between an axile hopping motion and a rotational motion about the center of mass of the robot; these are encoded by a hopping Spring-loaded Inverted Pendulum (SLIP) model and the rotation of a Flywheel, respectively. We thus present the Flywheel SLIP model for generating the desired motion on the ground phase. In the flight phase, we present a momentum transmission method to adjust the orientation of the lower body based on the conservation of the centroidal momentum. The generated motion plans are realized on the full-dimensional robot via momentum-included task space control. Finally, the proposed method is implemented on a modified version of the bipedal robot Cassie in simulation wherein multiple somersault motions are generated

Control Implementation of Dynamic Locomotion on Compliant, Underactuated, Force-Controlled Legged Robots with Non-Anthropomorphic Design

The control of locomotion on legged robots traditionally involves a robot that takes a standard legged form, such as the anthropomorphic humanoid, the dog-like quadruped, or the bird-like biped. Additionally, these systems will often be actuated with position-controlled servos or series-elastic actuators that are connected through rigid links. This work investigates the control implementation of dynamic, force-controlled locomotion on a family of legged systems that significantly deviate from these classic paradigms by incorporating modern, state-of-the-art proprioceptive actuators on uniquely configured compliant legs that do not closely resemble those found in nature. The results of this work can be used to better inform how to implement controllers on legged systems without stiff, position-controlled actuators, and also provide insight on how intelligently designed mechanical features can potentially simplify the control of complex, nonlinear dynamical systems like legged robots. To this end, this work presents the approach to control for a family of non-anthropomorphic bipedal robotic systems which are developed both in simulation and with physical hardware. The first is the Non-Anthropomorphic Biped, Version 1 (NABi-1) that features position-controlled joints along with a compliant foot element on a minimally actuated leg, and is controlled using simple open-loop trajectories based on the Zero Moment Point. The second system is the second version of the non-anthropomorphic biped (NABi-2) which utilizes the proprioceptive Back-drivable Electromagnetic Actuator for Robotics (BEAR) modules for actuation and fully realizes feedback-based force controlled locomotion. These systems are used to highlight both the strengths and weaknesses of utilizing proprioceptive actuation in systems, and suggest the tradeoffs that are made when using force control for dynamic locomotion. These systems also present case studies for different approaches to system design when it comes to bipedal legged robots

Whole-Body Dynamic Telelocomotion: A Step-to-Step Dynamics Approach to Human Walking Reference Generation

Teleoperated humanoid robots hold significant potential as physical avatars for humans in hazardous and inaccessible environments, with the goal of channeling human intelligence and sensorimotor skills through these robotic counterparts. Precise coordination between humans and robots is crucial for accomplishing whole-body behaviors involving locomotion and manipulation. To progress successfully, dynamic synchronization between humans and humanoid robots must be achieved. This work enhances advancements in whole-body dynamic telelocomotion, addressing challenges in robustness. By embedding the hybrid and underactuated nature of bipedal walking into a virtual human walking interface, we achieve dynamically consistent walking gait generation. Additionally, we integrate a reactive robot controller into a whole-body dynamic telelocomotion framework. Thus, allowing the realization of telelocomotion behaviors on the full-body dynamics of a bipedal robot. Real-time telelocomotion simulation experiments validate the effectiveness of our methods, demonstrating that a trained human pilot can dynamically synchronize with a simulated bipedal robot, achieving sustained locomotion, controlling walking speeds within the range of 0.0 m/s to 0.3 m/s, and enabling backward walking for distances of up to 2.0 m. This research contributes to advancing teleoperated humanoid robots and paves the way for future developments in synchronized locomotion between humans and bipedal robots.Comment: 8 pages, 8 figure

Understanding preferred leg stiffness and layered control strategies for locomotion

Despite advancement in the field of robotics, current legged robots still cannot achieve the kind of locomotion stability animals and humans have. In order to develop legged robots with greater stability, we need to better understand general locomotion dynamics and control principles. Here we demonstrate that a mathematical modeling approach could greatly enable the discovery and understanding of general locomotion principles. ^ It is found that animal leg stiffness when scaled by its weight and leg length falls in a narrow region between 7 and 27. Rarely in biology does such a universal preference exist. It is not known completely why this preference exists. Here, through simulation of the simple actuated-SLIP model, we show that the biological relative leg stiffness corresponds to the theoretical minimum of mechanical cost of transport. This strongly implies that animals choose leg stiffness in this region to reduce energetic cost. In addition, it is found that the stability of center-of-mass motion is also optimal when biological relative leg stiffness values are selected for actuated-SLIP. Therefore, motion stability could be another reason why animals choose this particular relative leg stiffness range. ^ We then extended actuated-SLIP by including realistic trunk pitching dynamics. At first, to form the Trunk Spring-Loaded Inverted Pendulum (Trunk-SLIP) model, the point mass of actuated-SLIP is replaced by a rigid body trunk while the leg remains massless and springy. It is found that exproprioceptive feedback during the flight phase is essential to the overall motion stability including trunk pitching. Either proprioceptive or exproprioceptive feedback during stance could generate stable running motion provided that exproprioceptive feedback is used during flight. When both kinds of feedback are used during stance, the overall stability is improved. However, stability with respect to speed perturbations remains limited. ^ Built upon Trunk-SLIP, we develop a model called extended Trunk-SLIP with trunk and leg masses. We then develop a hierarchical control strategy where different layers of control are added and tuned. When each layer is added, the overall motion stability is improved. This layer by layer strategy is simple in nature and allows quick controller design and tuning as only a limited number of control parameters needs to be added and tuned at each step. In the end, we propose a future control layer where the commanded speed is controlled to achieve a higher level target such as might be needed during smooth walking to running transitions. ^ In summary, we show here that the simple actuated-SLIP model is able to predict animal center-of-mass translation stability and overall mechanical cost of transport. More advanced models are then developed based upon actuated-SLIP. With a simple layer by layer control strategy, robust running motion can be discovered. Overall, this knowledge could help better understand locomotion dynamics in general. In addition, the developed control strategy could, in principle be applied to future hip based legged robot design

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. We first trained individual skills in isolation and then composed those skills end-to-end in a self-play setting. The resulting policy exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and transitions between them in a smooth, stable, and efficient manner - well beyond what is intuitively expected from the robot. The agents also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. The full range of behaviors emerged from a small set of simple rewards. Our agents were trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer, despite significant unmodeled effects and variations across robot instances. Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way. Indeed, even though the agents were optimized for scoring, in experiments they walked 156% faster, took 63% less time to get up, and kicked 24% faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives. Examples of the emergent behaviors and full 1v1 matches are available on the supplementary website.Comment: Project website: https://sites.google.com/view/op3-socce