(Georgia Institute of Technology, 2014-09)
Bhattacharjee, Tapomayukh; Grice, Phillip M.; Kapusta, Ariel; Killpack, Marc D.; Park, Daehyung; Kemp, Charles C.
We present a system that enables a robot to reach
locations in dense clutter using only haptic sensing. Our system
integrates model predictive control [1], learned initial conditions
[2], tactile recognition of object types [3], haptic mapping, and
geometric planning to efficiently reach locations using whole-
arm tactile sensing [4]. We motivate our work, present a system
architecture, summarize each component of the system, and
present results from our evaluation of the system reaching to
target locations in dense artificial foliage.
(Georgia Institute of Technology, 2013-10)
Bhattacharjee, Tapomayukh; Kapusta, Ariel; Rehg, James M.; Kemp, Charles C.
We demonstrate that data-driven methods can be used to rapidly categorize objects encountered through incidental contact on a robot arm. Allowing incidental contact with surrounding objects has benefits during manipulation such as increasing the workspace during reaching tasks. The information obtained from such contact, if available online, can potentially be used to map the environment and help in manipulation tasks. In this paper, we address this problem of online categorization using incidental contact during goal oriented motion. In cluttered environments, the detailed internal structure of clutter can be difficult to infer, but the environment type is often apparent. In a randomized cluttered environment of known object types and “outliers”, our approach uses Hidden Markov Models to capture the dynamic robot-environment interactions and to categorize objects based on the interactions. We combined leaf and trunk objects to create artificial foliage as a test environment. We collected data using a skin-sensor on the robot’s forearm while it reached into clutter. Our algorithm classifies the objects rapidly with low computation time and few data-samples. Using a taxel-by-taxel classification approach, we can successfully categorize simultaneous contacts with multiple objects and can also identify outlier objects in the environment based on the prior associated with an object’s likelihood in the given environment.