Learning from human demonstrations can facilitate automation but is risky because the execution of the learned policy might lead to collisions and other failures. Adding explicit constraints to avoid unsafe states is generally not possible when the state representations are complex. Furthermore, enforcing these constraints during execution of the learned policy can be challenging in environments where dynamics are difficult to model such as push mechanics in grasping. In this paper, we propose Derivative-Free Recovery (DFR), a two-phase method for generating robust policies from demonstrations in robotic manipulation tasks where the system comes to rest at each time step. In the first phase, we use support estimation of supervisor demonstrations and treat the support as implicit constraints on states. We also propose a time-varying modification for sequential tasks. In the second phase, we use this support estimate to derive a switching policy that employs the learned policy in the interior of the support and switches to a recovery policy to steer the robot away from the boundary of the support if it drifts too close. We present additional conditions, which linearly bound the difference in state at each time step by the magnitude of control, allowing us to prove that the robot will not violate the constraints using the recovery policy. A simulated pushing task in MuJoCo suggests that DFR can reduce collisions by 83 . On a physical line tracking task using a da Vinci Surgical Robot and a moving Stewart platform, DFR reduced collisions by 84 .
In surgical robotics, Yip and Camarillo @cite_21 studied model-free control of continuum manipulators in constrained environments where the constraints are initially unknown. The authors proposed a combined position and force controller which actively estimates Jacobians. Continuum manipulators in surgical environments are in general designed to conform" to obstacles constraints. In this paper, we consider manipulators in general constrained environments where the manipulator may not have direct force feedback from interacting with constraints.
@cite_21
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Continuum manipulators are designed to operate in constrained environments that are often unknown or unsensed, relying on body compliance to conform to obstacles. The interaction mechanics between the compliant body and unknown environment present significant challenges for traditional robot control techniques based on modeling these interactions exactly. In this letter, we describe a hybrid position force control method that uses a model-less approach. Model-less control is a recently-described approach to control that learns the continuum manipulator Jacobian and adapts to constraints in the environment in a safe manner. When interacting with the environment using the end-effector, these tip-constraints can affect the manipulator Jacobian estimates to become ill-conditioned. This letter addresses this issue by defining a hybrid control approach for simultaneously controlling end-effector position and forces while still maintaining the minimalist approach of model-less control. Under this method, continuum manipulators can safely and effectively interact with the environment, even when these interactions are arbitrary and unknown constraints.