Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with product variation and dynamic environments. In this paper, we present CoBT, a collaborative programming by demonstration framework for generating reactive and modular behavior trees. CoBT relies on a single demonstration and a combination of data-driven machine learning methods with logic-based declarative learning to learn a task, thus eliminating the need for programming expertise or long development times. The proposed framework is experimentally validated on 7 manipulation tasks and we show that CoBT achieves ≈ 93% success rate overall with an average of 7.5s programming time. We conduct a pilot study with non-expert users to provide feedback regarding the usability of CoBT. More videos and generated behavior trees are available at: https://github.com/jainaayush2006/CoBT.git.

We present a contact-implicit trajectory optimization framework that can plan contact-interaction trajectories for different robot architectures and tasks using a trivial initial guess and without requiring any parameter tuning. This is achieved by using a relaxed contact model along with an automatic penalty adjustment loop for suppressing the relaxation. Moreover, the structure of the problem enables us to exploit the contact information implied by the use of relaxation in the previous iteration, such that the solution is explicitly improved with little computational overhead. We test the proposed approach in simulation experiments for non-prehensile manipulation using a 7-DOF arm and a mobile robot and for planar locomotion using a humanoid-like robot in zero gravity. The results demonstrate that our method provides an out-of-the-box solution with good performance for a wide range of applications.

This paper presents a new method of maximizing the free space for a robot operating in a constrained environment under operator supervision. The objective is to make the resulting trajectories more robust to operator commands and/or changes in the environment. To represent the volume of free space, the constrained manipulability polytopes are used. These polytopes embed the distance to obstacles, the distance to joint limits and the distance to singular configurations. The volume of the resulting Cartesian polyhedron is used in an optimization-based motion planner to create the trajectories. Additionally, we show how fast collision-free inverse kinematic solutions can be obtained by exploiting the pre-computed inequality constraints. The proposed algorithm is validated in simulation and experimentally.

In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM facilitates the convergence of gradient-based optimization without compromising physical fidelity. On the other hand, the proposed SCvx-based approach combines the advantages of direct and shooting methods for CITO. For evaluations, we consider non-prehensile manipulation tasks. The proposed method is compared to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The results demonstrate that both methods can find physically-consistent motions that complete the tasks without a meaningful initial guess owing to the VSCM. The proposed SCvx-based method outperforms the iLQR-based method in terms of convergence, computation time, and the quality of motions found. Finally, the proposed SCvx-based method is tested on a standard robot platform and shown to perform efficiently for a real-world application.

A Mobile Cable-Driven Parallel Robot (MCDPR) is composed of a classical Cable-Driven Parallel Robot (CDPR) carried by multiple mobile bases. The additional mobilities due the motion of the mobile bases allow such systems to autonomously modify their geometric architecture, and thus make them suitable for multiple manipulation tasks in constrained environments. Moreover, these additional mobilities mean MCDPRs are kinematically redundant and may use this redundancy to optimize secondary task criteria. However, the high dimensional state space and closed chain constraints add complexity to the motion planning problem. To overcome this, we propose a method for trajectory planning for MCDPRs performing pick and place operations in cluttered environments by using direct transcription optimization. Two different scenarios have been considered and their results are validated using a dynamic simulation software (V-REP) and experimentally.

Cable-Driven Parallel Robots (CDPRs) have several advantages over conventional parallel manipulators most notably a large workspace. CDPRs whose workspace can be further increased by modification of the geometric architecture are known as Reconfigurable Cable Driven Parallel Robots(RCDPRs). A novel concept of RCDPRs, known as Mobile CDPR (MCDPR) that consists of a CDPR carried by multiple mobile bases, is studied in this paper. The system is capable of autonomously navigating to a desired location then deploying to a standard CDPR. In this paper, we analyze the Static equilibrium (SE) of the mobile bases when the system is fully deployed. In contrast to classical CDPRs we show that the workspace of the MCDPR depends, not only on the tension limits, but on the SE constraints as well. We demonstrate how to construct the Available Wrench Set (AWS) for a planar MCDPR wih a point-mass end-effector using both the convex hull and Hyperplane shifting methods. The obtained results are validated in simulation and on an experimental platform consisting of two mobile bases and a CDPR with four cables.

In this paper, we analyze the effects of contact models on contact-implicit trajectory optimization for manipulation. We consider three different approaches: (1)a contact model that is based on complementarity constraints, (2)a smooth contact model, and our proposed method (3) a variable smooth contact model. We compare these models in simulation in terms of physical accuracy, quality of motions, and computation time. In each case, the optimization process is initialized by setting all torque variables to zero, namely, without a meaningful initial guess. For simulations, we consider a pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our results demonstrate that the optimization based on the proposed variable smooth contact model provides a good trade-off between the physical fidelity and quality of motions at the cost of increased computation time.

In this paper, a force/vision control strategy is proposed for the robotic cutting of soft materials. The separation is performed by repeating a series of cuts, called passages, along a deformable three dimensional curve. An image based visual servoing system is proposed to control all six degrees of freedom of the cutting tool. This allows the system to change the cutting depth and angle in response to changes in the profile of the surface. The force is used to ensure the cut is performed cleanly without globally deforming the soft material. The proposed controller is validated experimentally by cutting soft foam material.

In this paper, a force/vision control strategy is proposed in order to separate soft deformable materials using cooperative robots. The separation is performed by repeating a series of cuts, called passages, along a curved trajectory. The vision control is used to locally update the robot trajectory in response to both on-line deformations and off-line modeling errors. The force controller is used to ensure that the cut is performed without global deformation or damage to the surrounding area. The second robot is used to facilitate the cutting by applying external forces to the object. The control scheme is validated experimentally by cutting soft foam material.