Physical reasoning in complex scenes is sensitive to mass

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 57-58).Many human activities require precise judgments about the dynamics and physical properties - for example, mass - of multiple objects. Classic work suggests that people's intuitive models of physics in mass-sensitive situations are relatively poor and error-prone, based on highly simplified heuristics that apply only in special cases. These conclusions seem at odds with the breadth and sophistication of naive physical reasoning in real-world situations. Our work measures the boundaries of people's physical reasoning in mass-sensitive scenarios and tests the richness of intuitive physics knowledge in more complex scenes. We asked participants to make quantitative judgments about stability and other physical properties of virtual 3D towers composed of heavy and light blocks. We found their judgments correlated highly with a model observer that uses simulations based on realistic physical dynamics and sampling-based approximate probabilistic inference to efficiently and accurately estimate these properties. Several alternative heuristic accounts provide substantially worse fits. In a separate task, participants observed virtual 3D billiards-like movies and judged which balls were lighter. In contrast to the previous experiments, we found their judgments to be more consistent with simple, visual heuristics than a simulation-based model that updates its beliefs about mass in response to prediction errors. We conclude that rich internal physics models are likely to play a key role in guiding human common-sense reasoning in prediction-based tasks and emphasize the need for further investigation in inference-based tasks.by Jessica B. Hamrick.M.Eng.and S.B

Nonlinear control of an exoskeleton seven degrees of freedom robot to realize an active and passive rehabilitation tasks

This doctoral thesis proposes the developments of an exoskeleton robot used to rehabilitate patients with upper-limb impairment, named ETS-MARSE robot. The developments included in this work are the design, and validation of a kinematic inverse solution and nonlinear control strategy for an upper limb exoskeleton robot. These approaches are used in passive and active rehabilitation motion in presence of dynamics and kinematics uncertainties and unexpected disturbances. Considering the growing population of post-stroke victims, there is a need to improve accessibility to physiotherapy by using the modern robotic rehabilitation technology. Recently, rehabilitation robotics attracted a lot of attention from the scientific community since it is able to overcome the limitations of conventional physical therapy. The importance of the rehabilitation robot lies in its ability to provide intensive physiotherapy for a long period time. The measured data of the robot allows the physiotherapist to accurately evaluate the patient’s performance. However, these devices are still part of an emerging area and present many challenges compared to the conventional robotic manipulators, such as the high nonlinearity, dimensional (high number of DOFs) and unknown dynamics (uncertainties). These limitations are provoked due to their complex mechanical structure designed for human use, the types of assistive motion, and the sensitivity of the interaction with a large diversity of human wearers. As a result, these conditions make the robot system vulnerable to dynamic uncertainties and external disturbances such as saturation, friction forces, backlash, and payload. Likewise, the interaction between human and the exoskeleton make the system subjected to external disturbances due to different physiological conditions of the subjects like the different weight of the upper limb for each subject. During a rehabilitation movement, the nonlinear uncertain dynamic model and external forces can turn into unknown function that can affect the performance of the exoskeleton robot. The main challenges addressed in this thesis are firstly to design a human inverse kinematics solution to perform a smooth movement similar to natural human movement (human-like motion). Secondly, to develop controllers characterized by a high-level of robustness and accuracy without any sensitivity to uncertain nonlinear dynamics and unexpected disturbances. This will give the control system more flexibility to handle the uncertainties and parameters’ variation in different modes of rehabilitation motion (passive and active)