Campus Mobility for the Future: The Electric Bicycle

Sustainable and practical personal mobility solutions for campus environments have traditionally revolved around the use of bicycles, or provision of pedestrian facilities. However many campus environments also experience traffic congestion, parking difficulties and pollution from fossil-fuelled vehicles. It appears that pedal power alone has not been sufficient to supplant the use of petrol and diesel vehicles to date, and therefore it is opportune to investigate both the reasons behind the continual use of environmentally unfriendly transport, and consider potential solutions. This paper presents the results from a year-long study into electric bicycle effectiveness for a large tropical campus, identifying barriers to bicycle use that can be overcome through the availability of public use electric bicycles

When perception says "no" to action: Approach cues make steep hills appear even steeper

Previous research has established that people's resources and action capabilities influence visual perception, and for example, make hills appear more or less steep. What has remained unexamined, however, is whether perception also changes when an action is impending. We propose that when action is expected in an environment that is challenging because it poses high energetic costs, perceptual estimates are increased. Experiment 1 showed that motor movements of approach led to steeper slant estimates than motor movements of avoidance, but only if participants were in good physical condition and thus capable of undertaking costly actions. Experiment 2 used a mindset priming task and found that approach resulted in higher slant estimates than either avoidance, or a neutral control condition, again for participants who were in good, but not for those in poor physical condition. Experiment 3 further showed that the approach cue on its own had the same effect as when combined with instructions that climbing was involved, thus suggesting that approach manipulations indeed implied the action of climbing. However, the effect of approach disappeared when climbing was explicitly ruled out. We suggest that inflated perceptual visual estimates in the face of challenging environments are adaptive because they discourage future actions that may be costly to perform

Autonomous Behaviors With A Legged Robot

Over the last ten years, technological advancements in sensory, motor, and computational capabilities have made it a real possibility for a legged robotic platform to traverse a diverse set of terrains and execute a variety of tasks on its own, with little to no outside intervention. However, there are still several technical challenges to be addressed in order to reach complete autonomy, where such a platform operates as an independent entity that communicates and cooperates with other intelligent systems, including humans. A central limitation for reaching this ultimate goal is modeling the world in which the robot is operating, the tasks it needs to execute, the sensors it is equipped with, and its level of mobility, all in a unified setting. This thesis presents a simple approach resulting in control strategies that are backed by a suite of formal correctness guarantees. We showcase the virtues of this approach via implementation of two behaviors on a legged mobile platform, autonomous natural terrain ascent and indoor multi-flight stairwell ascent, where we report on an extensive set of experiments demonstrating their empirical success. Lastly, we explore how to deal with violations to these models, specifically the robot\u27s environment, where we present two possible extensions with potential performance improvements under such conditions

Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks

This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm

RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation

A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the prevalent approach of generating 2D occupancy grids through raytracing makes the generated map unsafe to plan in, due to inaccurate representation of unknown space. Additionally, existing planners such as MPPI do not consider speeds in known free and unknown space separately, leading to slower overall plans. The RAMP pipeline proposed here solves these issues using new mapping and planning methods. This work first presents ground point inflation with persistent spatial memory as a way to generate accurate occupancy grid maps from classified pointclouds. Then we present an MPPI-based planner with embedded variability in horizon, to maximize speed in known free space while retaining cautionary penetration into unknown space. Finally, we integrate this mapping and planning pipeline with risk constraints arising from 3D terrain, and verify that it enables fast and safe navigation using simulations and hardware demonstrations.Comment: 7 pages submitted to ICRA 202

Evaluating affordances of streams and rivers pertaining to children functioning in natural environment

This study evaluates the affordances of natural water bodies pertaining to functioning of children. Ten children, aged 4-12, were observed experiencing three streams and two rivers in tropical environment. A phenomenological approach yielded a dataset of the children’s behavioral responses derived from a behavioral mapping and an open-ended interview. The responses are physical movement and words and phrases of the children suggesting their preferences or dislikes toward the water settings. The data was analyzed in three stages, firstly, positive or negative affordances, secondly, a taxonomy affordance of children’s outdoor environment, and thirdly, level of affordances. The children experienced 78 positive affordances and only five negative ones. From the taxonomy, the water afforded 11 categories of environmental qualities in which the categories graspable/detached objects and water offered the most number of affordances, 16 and 15, respectively. Most of the children’s activities were performatory and exploratory types. The results suggest that children perceived the affordances of streams and rivers through physical, cognitive and social interactions. The children, therefore, perceived the water bodies as playscapes affording varieties of functional meanings