Improved GelSight Tactile Sensor for Measuring Geometry and Slip

A GelSight sensor uses an elastomeric slab covered with a reflective membrane to measure tactile signals. It measures the 3D geometry and contact force information with high spacial resolution, and successfully helped many challenging robot tasks. A previous sensor, based on a semi-specular membrane, produces high resolution but with limited geometry accuracy. In this paper, we describe a new design of GelSight for robot gripper, using a Lambertian membrane and new illumination system, which gives greatly improved geometric accuracy while retaining the compact size. We demonstrate its use in measuring surface normals and reconstructing height maps using photometric stereo. We also use it for the task of slip detection, using a combination of information about relative motions on the membrane surface and the shear distortions. Using a robotic arm and a set of 37 everyday objects with varied properties, we find that the sensor can detect translational and rotational slip in general cases, and can be used to improve the stability of the grasp.Comment: IEEE/RSJ International Conference on Intelligent Robots and System

Food waste as a raw material for biofuel production

With the development of economy, people's living standard is constantly improving. People's demand for food is also increasing. With the increase of food consumption, the production of food waste is also increasing. Therefore, the increase of food waste results in the increasing proportion of food waste in urban garbage. Compared with other waste, food waste is more difficult to deal with and more harmful to the environment. Because the treatment technology of food waste is not perfect, it may cause secondary pollution in the process of treatment. Food waste has always been a concern of people and there are many organic compounds and nutrients in food waste. This part can be used to produce biofuels to solve the current energy crisis, it can also be a good way to deal with food waste to prevent food waste from polluting the environment. Starting from food waste, this paper introduces the definition, source and treatment of food waste. The treatment methods of food waste mainly include mixed landfill, incineration, anaerobic fermenta-tion, aerobic composting and livestock feed. Among them, the two treatment methods of mixed land-fill and incineration are banned by many countries because of their disadvantages. The following arti-cle introduces the definition, application and how to use food waste to produce biofuels. In this part, the production of biogas by anaerobic fermentation, transesterification and biodiesel, bioethanol and butanol by ABE fermentation are introduced respectively

Connecting Look and Feel: Associating the visual and tactile properties of physical materials

For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric sample, we captured color and depth images of draped fabrics along with tactile data from a high resolution touch sensor. We then sought to associate the information from vision and touch by jointly training CNNs across the three modalities. Through the CNN, each input, regardless of the modality, generates an embedding vector that records the fabric's physical property. By comparing the embeddings, our system is able to look at a fabric image and predict how it will feel, and vice versa. We also show that a system jointly trained on vision and touch data can outperform a similar system trained only on visual data when tested purely with visual inputs

GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, the depth of field of the camera, and the size of the tactile sensing area. The sensor sustains the wear from continuous use -- and abuse -- in grasping tasks by combining tougher materials for the compliant soft gel, a textured fabric skin, a structurally rigid body, and a calibration process that maintains homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that track the signal quality during more than 3000 grasping experiments.Comment: RA-L Pre-print. 8 page

Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning

Manipulation of deformable objects is a challenging task for a robot. It will be problematic to use a single sensory input to track the behaviour of such objects: vision can be subjected to occlusions, whereas tactile inputs cannot capture the global information that is useful for the task. In this paper, we study the problem of using vision and tactile inputs together to complete the task of following deformable linear objects, for the first time. We create a Reinforcement Learning agent using different sensing modalities and investigate how its behaviour can be boosted using visual-tactile fusion, compared to using a single sensing modality. To this end, we developed a benchmark in simulation for manipulating the deformable linear objects using multimodal sensing inputs. The policy of the agent uses distilled information, e.g., the pose of the object in both visual and tactile perspectives, instead of the raw sensing signals, so that it can be directly transferred to real environments. In this way, we disentangle the perception system and the learned control policy. Our extensive experiments show that the use of both vision and tactile inputs, together with proprioception, allows the agent to complete the task in up to 92% of cases, compared to 77% when only one of the signals is given. Our results can provide valuable insights for the future design of tactile sensors and for deformable objects manipulation.Comment: 8 pages, 11 figure