Robotic sketching in real-world scenarios poses a challenging problem with diverse applications in art, robotics, and digital design. We present a novel approach that bridges the gap between digital and robotic sketching, leveraging behavior cloning and reinforcement learning techniques. This paper introduces an approach aimed at bringing the gap between simulated and real-world robotic sketching closer together through the integration of behavior cloning and reinforcement learning techniques. Our approach trains painting policies that operate effectively in both virtual environments and real-world robotic sketching systems. We have implemented a robotic sketching system featuring an UltraArm robot equipped with a RealSense D415 camera, closely emulating the MyPaint virtual environment. Our system can perceive its environment and adapt painting policies to natural painting media. Our results highlight the effectiveness of our agent in terms of acquiring policies for high-dimensional continuous action spaces, enabling the seamless transfer of brush manipulation techniques from simulation to practical robotic sketching. Furthermore, we demonstrate our robotic sketching system’s capability to generate complex images and strokes using various configurations. https://sites.google.com/view/sketchingrobot

We present a centralized algorithm for labeled, disk-shaped Multi-Robot Path Planning (MPP) in a continuous planar workspace with polygonal boundaries. Our method automatically transform the continuous problem into a discrete, graph-based variant termed the pebble motion problem, which can be solved efficiently. To construct the underlying pebble graph, we identify inscribed circles in the workspace via a medial axis transform and organize robots into layers within each inscribed circle. We show that our layered pebble-graph enables collision-free motions, allowing all graph-restricted MPP instances to be feasible. MPP instances with continuous start and goal positions can then be solved via local navigations that route robots from and to graph vertices. We tested our method on several environments with high robot-packing densities (up to 61.6% of the workspace). For environments with narrow passages, such density violates the well-separated assumptions made by state-of-the-art MPP planners, while our method achieves an average success rate of 83%.

The droop control and virtual synchronous generator control are currently the two most popular inverter control strategies, but both of these control strategies cannot be satisfied at the same time: (1) Frequency stability in stand-alone (SA) mode. (2) In the grid-connected (GC) mode, the output active power quickly follows the input power command. (3) Anti-interference ability of inverter output frequency in gridconnected mode. In response to these deficiencies, this paper proposes an improved droop control based on multi-stage leadlag compensation. This control strategy adds a lag link to the frequency output link of the traditional droop control to provide virtual inertia for the system; multi-stage lead lag compensation is added to the active power output section to improve the dynamic characteristics in the grid-connected mode. In the GC state, the improved droop control effectively reduces the power impact when the power command changes, and accelerates the power response speed. Enhanced the anti-interference ability of output frequency. In SA state, improved droop control can provide sufficient inertia and damping characteristics. Finally, the simulation results verify the effectiveness of the proposed improved droop control.

We present an efficient algorithm for motion planning and controlling a robot system with a high number of degrees-of-freedom (DOF). These systems include high-DOF soft robots and articulated robots interacting with a deformable environment. We present a novel technique to accelerate the evaluations of the forward dynamics function by storing the results of costly computations in a hierarchical adaptive grid. Furthermore, we exploit the underactuated properties of the robot systems and build the grid in a low-dimensional space. Our approach approximates the forward dynamics function with guaranteed error bounds and can be used in optimization-based motion planning and reinforcement-learning-based feed-back control. We highlight the performance on two high-DOF robot systems: a line-actuated elastic robot arm and an underwater swimming robot in water. Compared to prior techniques based on exact dynamics evaluation, we observe one to two orders of magnitude improvement in the performance.

We present an algorithm for combining natural language processing (NLP) and fast robot motion planning to automatically generate robot movements. Our formulation uses a novel concept called Dynamic Constraint Mapping to transform complex, attribute-based natural language instructions into appropriate cost functions and parametric constraints for optimization-based motion planning. We generate a factor graph from natural language instructions called the Dynamic Grounding Graph (DGG), which takes latent parameters into account. The coefficients of this factor graph are learned based on conditional random fields (CRFs) and are used to dynamically generate the constraints for motion planning. We map the cost function directly to the motion parameters of the planner and compute smooth trajectories in dynamic scenes. We highlight the performance of our approach in a simulated environment and via a human interacting with a 7-DOF Fetch robot using intricate language commands including negation, orientation specification, and distance constraints.

The complex physical properties of highly deformable materials such as clothes pose significant challenges for autonomous robotic manipulation systems. We present a novel visual feedback dictionary-based method for manipulating deformable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.