Learning to Represent Haptic Feedback for Partially-Observable Tasks

The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.Comment: IEEE International Conference on Robotics and Automation (ICRA), 201

Synthesizing Manipulation Sequences for Under-Specified Tasks using Unrolled Markov Random Fields

Abstract — Many tasks in human environments require performing a sequence of navigation and manipulation steps involving objects. In unstructured human environments, the location and configuration of the objects involved often change in unpredictable ways. This requires a high-level planning strategy that is robust and flexible in an uncertain environment. We propose a novel dynamic planning strategy, which can be trained from a set of example sequences. High level tasks are expressed as a sequence of primitive actions or controllers (with appropriate parameters). Our score function, based on Markov Random Field (MRF), captures the relations between environment, controllers, and their arguments. By expressing the environment using sets of attributes, the approach generalizes well to unseen scenarios. We train the parameters of our MRF using a maximum margin learning method. We provide a detailed empirical validation of our overall framework demonstrating successful plan strategies for a variety of tasks. 1 I

Unstructured Human Activity Detection from RGBD Images

Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and pointcloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set.Comment: 2012 IEEE International Conference on Robotics and Automation (A preliminary version of this work was presented at AAAI workshop on Pattern, Activity and Intent Recognition, 2011

Bubble formation in globe valve and flow characteristics of partially filled pipe water flow

Air bubble entrainment is a phenomenon that can significantly reduce the efficiency of liquid motion in piping systems. In the present study, the bubble formation mechanism in a globe valve with 90% water fraction flow is explained by visualization study and pressure oscillation analysis. The shadowgraph imaging technique is applied to illustrate the unsteady flow inside the transparent valve. This helps to study the effect of bubbles induced by the globe valve on pressure distribution and valve flow coefficient. International Society of Automation (ISA) recommends locations for measuring pressure drop of the valve to determine its flow coefficient. This paper presents the comparison of the pressures at different locations along with the upstream and the downstream of the valve with the values at recommended positions by the ISA standard. The results show that in partially filled pipe flow, the discrepancies in pressure between different measurement locations in the valve downstream are significant at valve openings less than 30%. The aerated flow induces the oscillation in pressure and flow rate, which leads to the fluctuation in the flow coefficient of the valve. The flow coefficients have a linear relationship with the Reynolds number. For the same increase of Reynolds number, the flow coefficients grow faster with larger valve openings and level off at the opening of 50%

Predictive biomarkers for 5-fluorouracil and oxaliplatin-based chemotherapy in gastric cancers via profiling of patient-derived xenografts.

Gastric cancer (GC) is commonly treated by chemotherapy using 5-fluorouracil (5-FU) derivatives and platinum combination, but predictive biomarker remains lacking. We develop patient-derived xenografts (PDXs) from 31 GC patients and treat with a combination of 5-FU and oxaliplatin, to determine biomarkers associated with responsiveness. When the PDXs are defined as either responders or non-responders according to tumor volume change after treatment, the responsiveness of PDXs is significantly consistent with the respective clinical outcomes of the patients. An integrative genomic and transcriptomic analysis of PDXs reveals that pathways associated with cell-to-cell and cell-to-extracellular matrix interactions enriched among the non-responders in both cancer cells and the tumor microenvironment (TME). We develop a 30-gene prediction model to determine the responsiveness to 5-FU and oxaliplatin-based chemotherapy and confirm the significant poor survival outcomes among cases classified as non-responder-like in three independent GC cohorts. Our study may inform clinical decision-making when designing treatment strategies

Learning to Manipulate Novel Objects for Assistive Robots

The ability to reason about different modalities of information, for the purpose of physical interaction with objects, is a critical skill for assistive robots. For a robot to be able to assist us in our daily lives, it is not feasible to train each robot for a large number of tasks with all instances of objects that exist in human environments. Robots will have to generalize their skills by jointly reasoning with various sensor modalities such as vision, language and haptic feedback. This is an extremely challenging problem because each modality has intrinsically different statistical properties. Moreover, even with expert knowledge, manually designing joint features between such disparate modalities is difficult. In this dissertation, we focus on developing learning algorithms for robots that model tasks involving interactions with various objects in unstructured human environments --- especially on novel objects and scenarios that involve sequences of complicated manipulation. To this end, we develop algorithms that learn shared representations of multimodal data and model full sequences of complex motions. We demonstrate our approach on several different applications: understanding human activities in unstructured environment, synthesizing manipulation sequences for under-specified tasks, manipulating novel appliances, and manipulating objects with haptic feedback