Autonomous Robots

This paper presents a control strategy, biologically-inspired postural reflex, based directly on animal behaviors, which allows a quadrupedal robot to walk up and down hill smoothly. A central pattern generator (CPG) and a hip-to-knee mapping function are employed to realize the basic rhythmic motion for the quadrupedal robot. The trunk pitch angle feedback of the robot is linearly introduced to the CPG, spontaneously changing the mid-positions of the four legs to make postural adjustments as the way a cat does. Thereby, slipping and falling-down are greatly reduced. Numerical simulations and experimental implementation on a physical quadrupedal robot demonstrate the effectiveness of the proposed approach.

Most hovering aircraft such as helicopters and animal-inspired flapping-wing flyers are dynamically unstable in flight, quickly tumbling in the absence of feedback control. The addition of feedback loops can stabilize, but at the cost of additional sensing and actuation components. This can add expense, weight, and complexity. An alternative to feedback is the use of passive mechanisms such as aerodynamic drag to stabilize attitude. Previous work has suggested that small aircraft can be stabilized by adding air dampers above and below the center of mass. We present flight tests of an insect-scale robot operating under this principle. When controlled to a constant altitude, it remains stably upright while undergoing cyclic attitude oscillations. To characterize these oscillations, we present a nonlinear analytic model derived from first principles that reproduces the observed behavior. Using numerical simulation, we analyze how changing damper size, position, mass, and midpoint offset affect these oscillations, building on previous work that considered only a single configuration. Our results indicate that only by increasing damper size can lateral oscillation amplitude be significantly reduced, at the cost of increased damper mass. Additionally, we show that as scale diminishes, the damper size must get relatively larger. This suggests that smaller damper-equipped robots must operate in low-wind areas or in boundary-layer flow near surfaces.

We present a method for autonomous learning of dextrous manipulation skills with multifingered robot hands. We use heuristics derived from observations made on human hands to reduce the degrees of freedom of the task and make learning tractable. Our approach consists of learning and storing a few basic manipulation primitives for a few prototypical objects and then using an associative memory to obtain the required parameters for new objects and/or manipulations. The parameter space of the robot is searched using a modified version of the evolution strategy, which is robust to the noise normally present in real-world complex robotic tasks. Given the difficulty of modeling and simulating accurately the interactions of multiple fingers and an object, and to ensure that the learned skills are applicable in the real world, our system does not rely on simulation; all the experimentation is performed by a physical robot, in this case the 16-degree-of-freedom Utah/MIT hand. Experimental results show that accurate dextrous manipulation skills can be learned by the robot in a short period of time. We also show the application of the learned primitives to perform an assembly task and how the primitives generalize to objects that are different from those used during the learning phase.

Loop closure detection is one of the most important module in Simultaneously Localization and Mapping (SLAM) because it enables to find the global topology among different places. A loop closure is detected when the current place is recognized to match the previous visited places. When the SLAM is executed throughout a long-term period, there will be additional challenges for the loop closure detection. The illumination, weather, and vegetation conditions can often change significantly during the life-long SLAM, resulting in the critical strong perceptual aliasing and appearance variation problems in loop closure detection. In order to address this problem, we propose a new Robust Multimodal Sequence-based (ROMS) method for robust loop closure detection in long-term visual SLAM. A sequence of images is used as the representation of places in our ROMS method, where each image in the sequence is encoded by multiple feature modalites so that different places can be recognized discriminatively. We formulate the robust place recognition problem as a convex optimization problem with structured sparsity regularization due to the fact that only a small set of template places can match the query place. In addition, we also develop a new algorithm to solve the formulated optimization problem efficiently, which guarantees to converge to the global optima theoretically. Our ROMS method is evaluated through extensive experiments on three large-scale benchmark datasets, which record scenes ranging from different times of the day, months, and seasons. Experimental results demonstrate that our ROMS method outperforms the existing loop closure detection methods in long-term SLAM, and achieves the state-of-the-art performance.

With the aim of attaining increased fluency and efficiency in human-robot teams, we have developed a cognitive architecture for robotic teammates based on the neuro-psychological principles of anticipation and perceptual simulation through top-down biasing. An instantiation of this architecture was implemented on a non-anthropomorphic robotic lamp, performing a repetitive human-robot collaborative task. In a human-subject study in which the robot works on a joint task with untrained subjects, we find our approach to be significantly more efficient and fluent than in a comparable system without anticipatory perceptual simulation. We also show the robot and the human to improve their relative contribution at a similar rate, possibly playing a part in the human’s “like-me” perception of the robot. In self-report, we find significant differences between the two conditions in the sense of team fluency, the team’s improvement over time, the robot’s contribution to the efficiency and fluency, the robot’s intelligence, and in the robot’s adaptation to the task. We also find differences in verbal attitudes towards the robot: most notably, subjects working with the anticipatory robot attribute more human qualities to the robot, such as gender and intelligence, as well as credit for success, but we also find increased self-blame and self-deprecation in these subjects’ responses. We believe that this work lays the foundation towards modeling and evaluating artificial practice for robots working in collaboration with humans.

The advantage of modular self-reconfigurable robot systems is their flexibility, but this advantage can only be realized if appropriate configurations (shapes) and behaviors (controlling programs) can be selected for a given task. In this paper, we present an integrated system for addressing high-level tasks with modular robots, and demonstrate that it is capable of accomplishing challenging, multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) a high-level mission planner, (2) a large design library spanning a wide set of functionality, (3) a design and simulation tool for populating the library with new configurations and behaviors, and (4) modular robot hardware. This paper builds on earlier work by Jing et al. (in: Robotics: science and systems, 2016), extending the original system to include environmentally adaptive parametric behaviors, which integrate motion planners and feedback controllers with the system.