Tata Consultancy Services Limited

Snehasis Banerjee

Brojeshwar Bhowmick

Arpan Pal

Chayan Sarkar

Prateep Misra

Pradip Pramanick

Debnath Mukherjee

Balamuralidhar Purushothaman

Tanushyam Chattopadhyay

Abhijan Bhattacharyya

Ruddra Dev Roychoudhury

Ashis Sau

Aniruddha Sinha

Avik Ghose

Soma Bandyopadhyay

Puneet Kumar Agarwal

Arijit Mukherjee

Arijit Ukil

Swagata Biswas

Kritika Anand

Mariswamy Girish Chandra

Swarnava Dey

Vivek Chandel

Ayan Mukherjee

Hrishav Bakul Barua

Dipanjan Das

Sayan Paul

Soumyadip Maity

Utpal Garain

Ishan Sahu

Gulshan Kumar

Sai Shankar Narasimhan

Madhava Krishna Krishnan

Satanik Panda

Abhisek Das

Ruddra Roy Chowdhury

Mritunjoy Halder

Utpal Garin

Siddharth Bhattacharya

Indrajit Bhattachyarya

A system and method for navigation of a robot from a first area to a second area in a facility is provided. The present disclosure is providing robot navigation using the 'Areagoal' Navigation technique. ‘Areagoal’ class of problem is divided into two subtasks: identifying the area; and navigation from one area to another. The robot starts in first location and goes out of the current area if it is not in the target area. If there are multiple openings from the first area, it needs to select the most statistically close one to the target area and go there. If the target area is not reached, it backtracks to an earlier viable branch position to continue the target area search. The system takes input from RGB-D camera and odometer, while the output is action space (left, right, forward) with goal of moving to target area.

Method and system for navigation of robot from one area to another area

This disclosure relates generally to systems and methods for object detection using a geometric semantic map based robot navigation using an architecture to empower a robot to navigate an indoor environment with logical decision making at each intermediate stage. The decision making is further enhanced by knowledge on actuation capability of the robots and that of scenes, objects and their relations maintained in an ontological form. The robot navigates based on a Geometric Semantic map which is a relational combination of geometric and semantic map. In comparison to traditional approaches, the robot's primary task here is not to map the environment, but to reach a target object. Thus, a goal given to the robot is to find an object in an unknown environment with no navigational map and only egocentric RGB camera perception.

Systems and methods for object detection using a geometric semantic map based robot navigation

A model for semantic navigation for service robots to find out-of-view objects in an indoor environment is provided. Initially, the system receives a target object to be reached by the mobile robot in the indoor environment. Further, a current location of the mobile robot is identified by a localization technique. An embedding corresponding to each of a plurality of visible regions is computed using a pretrained Graph Neural Network GNN. The GNN is pretrained using a trajectory data and a spatial relationship graph associated with the indoor environment. Further, a similarity score is computed for each of the plurality of visible regions based on the corresponding embedding using a scoring technique. An optimal visible region is identified by comparing the similarity score. Finally, a next action to be taken by the mobile robot selected from a plurality of actions based on the optimal visible region.

Method and system for semantic navigation using spatial graph and trajectory history

Technical challenge in unusual human activity detection task is to rightly identify only unexpected or unusual movements from constant regular movements present in a scene, with most techniques built on understanding that camera is static. However, ego view camera of mobile surveillance robot is in motion as robot navigates. Embodiments herein provide a method and system for anomalous activity detection for mobile surveillance robots by mimicking 'Konio-Parvocellular-Magno' cells of the human brain into a NN model, which are responsible for detecting slow, normal, and swift changes in perceived scenes. To detect anomalous activity, the static or normal movements of scene captured by ego view camera are identified as redundant information and only RoI is forwarded for further processing using the Optical flow and SSIM techniques. The NN model mimicking KPM is trained only on the RoI to detect normal or anomalous activity.

Anomalous activity detection for mobile surveillance robots

The embodiments of present disclosure herein address unresolved problem of cognitive navigation strategies for a telepresence robotic system. This includes giving instruction remotely over network to go to a point in an indoor space, to go an area, to go to an object. Also, human robot interaction to give and understand interaction is not integrated in a common telepresence framework. The embodiments herein provide a telepresence robotic system empowered with a smart navigation which is based on in situ intelligent visual semantic mapping of the live scene captured by a robot. It further presents an edge-centric software architecture of a teledrive comprising a speech recognition based HRI, a navigation module and a real-time WebRTC based communication framework that holds the entire telepresence robotic system together. Additionally, the disclosure provides a robot independent API calls via device driver ROS, making the offering hardware independent and capable of running in any robot.

Telepresence robots having cognitive navigation capability

Existing cognitive robotic applications follow a practice of building specific applications for specific use cases. However, the knowledge of the world and the semantics are common for a robot for multiple tasks. In this disclosure, to enable usage of knowledge across multiple scenarios, a method and system for ontology guided indoor scene understanding for cognitive robotic tasks is described where in scenes are processed based on techniques filtered based on querying ontology with relevant objects in perceived scene to generate a semantically rich scene graph. Herein, an initially manually created ontology is updated and refined in online fashion using external knowledge-base, human robot interaction and perceived information. This knowledge helps in semantic navigation, aids in speech, and text based human robot interactions.

System and method for ontology guided indoor scene understanding for cognitive robotic tasks

Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.

Knowledge partitioning for task execution by conversational tele-presence robots in a geographically separated environment

One of the major artifacts that pushed Information Technology companies ahead of its competitors is undoubtedly contextual domain knowledge. When a new development problem comes to an IT team, how problem solving and steps of action can be automatically formulated is the major area of research. A method and system for utilizing domain knowledge to identify solution to a problem has been provided. The problem is reformulated as recommending a workflow like a pipeline of connected steps, by leveraging contextual domain knowledge and technical knowledge, finally planning and scheduling solutions steps, given a problem of a domain & use case. This is achieved by Contextual sequence-aware recommendation of steps, backed by semantic web technologies and pattern recognition steps. Finally a plan is derived by automated planning techniques which can be executed based on software orchestration by connecting a repository of re-usable annotated code blocks.

Method and system for utilizing domain knowledge to automatically identify solution to a problem

Conventionally, Received Signal Strength Indicator (RSSI)-based solutions have been extensively devised in the domains of indoor localization and context-aware applications. These solutions are primarily based on a path-loss attenuation model, with customizations on RSSI processing and are usually regression-based. Further, existing solutions for distance and proximity estimation incorporate data features derived only from the RSSI values themselves with additional features like frequency of occurrence of certain RSSI values thus are less accurate. Present disclosure provides systems and methods that implement a classification model that uses RSSI as well as temporal features derived from the received data packets. The model uses data from multiple devices in different environments for training and can execute proximity decisions on the device itself. The method of the present disclosure monitoring proximity between a plurality of devices implements/uses an effective protocol for decision aggregation to suppress false positive proximity events generated and further stabilizes device's response.

Real-time monitoring of proximity between a plurality of computing devices

The disclosure herein relates to methods and systems for enabling human-robot interaction (HRI) to resolve task ambiguity. Conventional techniques that initiates continuous dialogue with the human to ask a suitable question based on the observed scene until resolving the ambiguity are limited. The present disclosure use the concept of Talk-to-Resolve (TTR) which initiates a continuous dialogue with the user based on visual uncertainty analysis and by asking a suitable question that convey the veracity of the problem to the user and seek guidance until all the ambiguities are resolved. The suitable question is formulated based on the scene understanding and the argument spans present in the natural language instruction. The present disclosure asks questions in a natural way that not only ensures that the user can understand the type of confusion, the robot is facing; but also ensures minimal and relevant questioning to resolve the ambiguities.

Methods and systems for enabling human-robot interaction to resolve task ambiguity

Systems and methods for obtaining optimal mother wavelets for facilitating machine learning tasks. The traditional systems and methods provide for selecting a mother wavelet and signal classification using some traditional techniques and methods but none them provide for selecting an optimal mother wavelet to facilitate machine learning tasks. Embodiments of the present disclosure provide for obtaining an optimal mother wavelet to facilitate machine learning tasks by computing values of energy and entropy based upon labelled datasets and a probable set of mother wavelets, computing values of centroids and standard deviations based upon the values of energy and entropy, computing a set of distance values and normalizing the set of distance values and obtaining the optimal mother wavelet based upon the set of distance values for performing a wavelet transform and further facilitating machine learning tasks by classifying or regressing, a new set of signal classes, corresponding to a new set of signals.

Systems and methods for obtaining optimal mother wavelets for facilitating machine learning tasks

Systems and methods for extending reasoning capability for data analytics in Internet of Things (IoT) platform(s) are provided. Traditional systems and methods for executing IoT analytics tasks suffer as IoT analytics techniques are generated in different programming language platforms, and this leads to a manual intervention or an asynchronous and sequential analysis of IoT analytics task(s). Embodiments of the method disclosed provide for overcoming the limitations faced by the traditional systems and methods by dynamically creating procedural functions from a plurality of programming languages upon determining an absence of pre-defined procedural functions, and extracting, using the dynamically created procedural functions, one or more semantic rules in a real-time, wherein the one or more semantic rules extend a reasoning capability for executing the one or more data analytics tasks in a plurality of IoT platforms.

Systems and methods for extending reasoning capability for data analytics in internet-of-things (IoT) platform

Signal analysis is applied in various industries and medical field. In signal analysis, wavelet analysis plays an important role. The wavelet analysis needs to identify a mother wavelet associated with an input signal. However, identifying the mother wavelet associated with the input signal in an automatic way is challenging. Systems and methods of the present disclosure provides signal analysis with automatic selection of wavelets associated with the input signal. The method provided in the present disclosure receives the input signal and a set of parameters associated with the signal. Further, the input signal is analyzed converted into waveform. The waveforms are analyzed to provide image units. Further, the image units are processed by a plurality of deep architectures. The deep architectures provides a set of comparison scores and a matching wavelet family is determined by utilizing the set of comparison scores.

System and method for signal analysis

Development of sensor data based descriptive and prescriptive system involves machine learning tasks like classification and regression. Any such system development requires the involvement of different stake-holders for obtaining features. Such features typically obtained are not interpretable for 1-D sensor signals. Embodiments of the present disclosure provide systems and methods that perform signal analysis for features extraction and interpretation thereof wherein input is raw signal data where origin of a feature is traced to signal data, and mapped to domain/application knowledge. Feature(s) are extracted using deep learning network(s) and machine learning (ML) model(s) are implemented for sensor data analysis to perform causality analysis for prognostics. Layer(s) (say last layer) of Deep Network(s) contains the automatically derived features that can be used for ML tasks. Parameter(s) tuning is performed based on the set of features that were recommended by the system to determined performance of systems (or applications) under consideration.

Signal analysis systems and methods for features extraction and interpretation thereof

A method and system has been provided for recommending features for developing an IoT analytics application. The method follows a deep like architecture. It comprises of three distinct layers. First layer is for input signal processing and other two layers are for feature reduction. The time domain, frequency domain and time-frequency domain features are extracted from the input signal. The invention uses multiple feature selection methods so that the union of the recommended features by these feature selection methods is significantly lesser than the initial set of features. The best feature combination is recommended using an exhaustive search.

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Kolkata, India

Snehasis Banerjee