Aligning Robot and Human Representations

To act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or mug orientation in its behavior. However, if we want robots to act for and with people, their representations must not be just functional but also reflective of what humans care about, i.e. they must be aligned. We observe that current learning approaches suffer from representation misalignment, where the robot's learned representation does not capture the human's representation. We suggest that because humans are the ultimate evaluator of robot performance, we must explicitly focus our efforts on aligning learned representations with humans, in addition to learning the downstream task. We advocate that current representation learning approaches in robotics should be studied from the perspective of how well they accomplish the objective of representation alignment. We mathematically define the problem, identify its key desiderata, and situate current methods within this formalism. We conclude by suggesting future directions for exploring open challenges.Comment: 14 pages, 3 figures, 1 tabl

Modeling, Simulation and Control of the Walking of Biped Robotic Devices—Part III: Turning while Walking

In part II of this group of papers, the control of the gait of a biped robot during rectilinear walk was considered. The modeling approach and simulation, using Kane’s method with implementation leveraged by Autolev, a symbolic computational environment that is complementary, was discussed in part I. Performing turns during the walk is technically more complex than the rectilinear case and deserves further investigation. The problem is solved in the present part III as an extension of part II. The robot executes a rectilinear walk on a local reference frame whose progression axis is always tangent, and its origin performs the involute of the path curve. The curve is defined by its curvature (osculating circle) and center of curvature (evolute) along the path. Radius of curvature and center can change continuously (in practice at every sampling time). For postural equilibrium, Center of Gravity and Zero Moment Point (COG/ZMP) follow the same preview reference proposed for rectilinear walk (c o g R e f x ( t ) , c o g ˙ R e f x ( t ), c o g R e f y ( t ) , c o g ˙ R e f y ( t )). The effect of the turn on the sagittal plane is negligible and is ignored, while on the frontal plane it is accounted for by an offset on COG reference to compensate for the centrifugal acceleration. The body trunk and local frame rotation, and the generation of the references on this moving frame of the free foot trajectory during the swing deserve attention

Strong Induction in Hardware Model Checking

Symbolic Model checking is a widely used technique for automated verification of both hardware and software systems. Unbounded SAT-based Symbolic Model Checking (SMC) algorithms are very popular in hardware verification. The principle of strong induction is one of the first techniques for SMC. While elegant and simple to apply, properties as such can rarely be proven using strong induction and when they can be strengthened, there is no effective strategy to guess the depth of induction. It has been mostly displaced by techniques that compute inductive strengthenings based on interpolation and property directed reachability (PDR). In this thesis, we prove that strong induction is more concise than induction. We then present kAvy, an SMC algorithm that effectively uses strong induction to guide interpolation and PDR-style incremental inductive invariant construction. Unlike pure strong induction, kAvy uses PDR-style generalization to compute and strengthen an inductive trace. Unlike pure PDR, kAvy uses relative strong induction to construct an inductive invariant. The depth of induction is adjusted dynamically by minimizing a proof of unsatisfiability. We have implemented kAvy within the Avy Model Checker and evaluated it on HWMCC instances. Our results show that kAvy is more effective than both Avy and PDR, and that using strong induction leads to faster running time and solving more instances. Further, on a class of benchmarks, called shift, kAvy is orders of magnitude faster than Avy, PDR and pure strong induction

Design and Development of a Surgical Robot for Needle-Based Medical Interventions

Lung cancer is the leading cause of cancer related deaths. If diagnosed in a timely manner, the treatment of choice is surgical resection of the cancerous lesions followed by radiotherapy. However, surgical resection may be too invasive for some patients due to old age or weakness. An alternative is minimally invasive needle-based interventions for cancer diagnosis and treatment. This project describes the design, analysis, development and experimental evaluation of a modular, compact, patient-mounted robotic manipulator for lung cancer diagnosis and treatment. In this regard, a novel parallel Remote Centre of Motion (RCM) mechanism is proposed for minimally invasive delivery of needle-based interventions. The proposed robot provides four degrees of freedom (DOFs) to orient and move a surgical needle within a spherical coordinate system. There is an analytical solution for the kinematics of the proposed parallel mechanism and the end-effectors motion is well-conditioned within the required workspace. The RCM is located beneath the skin surface to minimize the invasiveness of the surgical procedure while providing the required workspace to target the cancerous lesions. In addition, the proposed robot benefits from a design capable of measuring the interaction forces between the needle and the tissue. The experimental evaluation of the robot has proved its capability to accurately orient and move a surgical needle within the required workspace. Although this robotic system has been designed for the treatment of lung cancer, it is capable of performing other procedures in the thoracic or abdominal cavity such as liver cancer diagnosis and treatment