ALPHRED: A Multi-Modal Operations Quadruped Robot for Package Delivery Applications

Hooks, Joshua; Ahn, Min Sung; Yu, Jeffrey; Zhang, Xiaoguang; Zhu, Taoyuanmin; Chae, Hosik; Hong, Dennis

doi:10.1109/lra.2020.3007482

Cited by 59 publications

(31 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One widely used method Hooks et al (2020), Ahn and Hong (2020), Bledt et al (2018) known as Raibert heuristics Raibert (1986) is often used in legged robots to determine the next footstep position:…”

Section: Footstep Position Selectionmentioning

confidence: 99%

“…One widely used method Hooks et al (2020) , Ahn and Hong (2020) , Bledt et al (2018) known as Raibert heuristics Raibert (1986) is often used in legged robots to determine the next footstep position:

where x f is the next footstep position with respect to the center of mass, T s is the duration of the phase, and

is the forward speed. The heuristic assumes the robot as a linear inverted pendulum and regulates the robot’s CoM velocity by its foot placement.…”

Section: Motion Analysismentioning

confidence: 99%

See 1 more Smart Citation

BALLU2: A Safe and Affordable Buoyancy Assisted Biped

et al. 2021

Self Cite

View full text Add to dashboard Cite

This work presents the first full disclosure of BALLU, Buoyancy Assisted Lightweight Legged Unit, and describes the advantages and challenges of its concept, the hardware design of a new implementation (BALLU2), a motion analysis, and a data-driven walking controller. BALLU is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to the lightweight body, which solves many issues that hinder current robots from operating close to humans. The advantages gained also lead to the platform’s distinct difficulties caused by severe nonlinearities and external forces such as buoyancy and drag. The paper describes the nonconventional characteristics of BALLU as a legged robot and then gives an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process using Spearman Correlation Coefficient is proposed to form low-dimensional state vectors from the simulation data, and an artificial neural network-based controller is trained on the same data. The controller is tested both on simulation and on real-world hardware. Its performance is assessed by observing the robot’s limit cycles and trajectories in the Cartesian coordinate. The controller generates periodic walking sequences in simulation as well as on the real-world robot even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLU’s walking.

show abstract

Section: Footstep Position Selectionmentioning

confidence: 99%

“…One widely used method Hooks et al (2020) , Ahn and Hong (2020) , Bledt et al (2018) known as Raibert heuristics Raibert (1986) is often used in legged robots to determine the next footstep position:

where x f is the next footstep position with respect to the center of mass, T s is the duration of the phase, and

is the forward speed. The heuristic assumes the robot as a linear inverted pendulum and regulates the robot’s CoM velocity by its foot placement.…”

Section: Motion Analysismentioning

confidence: 99%

BALLU2: A Safe and Affordable Buoyancy Assisted Biped

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Inspired by this ability, a few approaches have suggested utilizing the legs as manipulators to achieve dexterity with minimal hardware modification. In [13], [14], two limbs are used to pick up a box and the other two limbs for stance balancing. For multi-legs robots such as hexapod robots, four limbs are used balancing while the other two limbs are manipulating objects [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Circus ANYmal: A Quadruped Learning Dexterous Manipulation with Its Limbs

Shi

Homberger

Lee

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Quadrupedal robots are skillful at locomotion tasks while lacking manipulation skills, not to mention dexterous manipulation abilities. Inspired by the animal behavior and the duality between multi-legged locomotion and multi-fingered manipulation, we showcase a circus ball challenge on a quadrupedal robot, ANYmal. We employ a model-free reinforcement learning approach to train a deep policy that enables the robot to balance and manipulate a light-weight ball robustly using its limbs without any contact measurement sensor. The policy is trained in the simulation, in which we randomize many physical properties with additive noise and inject random disturbance force during manipulation, and achieves zero-shot deployment on the real robot without any adjustment. In the hardware experiments, dynamic performance is achieved with a maximum rotation speed of 15 • /s, and robust recovery is showcased under external poking. To our best knowledge, it is the first work that demonstrates the dexterous dynamic manipulation on a real quadrupedal robot.

show abstract

“…Recently, object transportation has come to occupy an important position in logistics [1], exploration [2], and service robotics fields [3]. Traditionally, many researchers have tried to study object transportation techniques by imitating nature's behavior models.…”

Section: Introductionmentioning

confidence: 99%

Automatic Curriculum Design for Object Transportation Based on Deep Reinforcement Learning

Eoh¹,

Park

2021

IEEE Access

View full text Add to dashboard Cite

This paper presents an automatic curriculum learning (ACL) method for object transportation based on deep reinforcement learning (DRL). Previous studies on object transportation using DRL have a sparse reward problem that an agent receives a rare reward for only the transportation completion of an object. Generally, curriculum learning (CL) has been used to solve the sparse reward problem. However, the conventional CL methods should be manually designed by users, which is difficult and tedious work. Moreover, there were no standard CL methods for object transportation. Therefore, we propose an ACL method for object transportation in which human intervention is unnecessary at the training step. A robot automatically designs curricula itself and iteratively trains according to the curricula. First, we define the difficult level of object transportation using a map, which is determined by the predicted travelling distance of an object and the existence of obstacles and walls. In the beginning, a robot learns the object transportation at an easy level (i.e., travelling distance is short and there are less obstacles around), then learns a difficult task (i.e., the long travelling distance of an object is required and there are many obstacles around). Second, training time also affects the performance of object transportation, and thus, we suggest an adaptive determining method of the number of training episodes. The number of episodes for training is adaptively determined based on the current success rate of object transportation. We verified the proposed method in simulation environments, and the success rate of the proposed method was 14% higher than nocurriculum. Also, the proposed method showed 63% (maximum) and 14% (minimum) higher success rates compared with the manual curriculum methods. Additionally, we conducted real experiments to verify the gap between simulation and practical results.

show abstract

ALPHRED: A Multi-Modal Operations Quadruped Robot for Package Delivery Applications

Cited by 59 publications

References 24 publications

BALLU2: A Safe and Affordable Buoyancy Assisted Biped

BALLU2: A Safe and Affordable Buoyancy Assisted Biped

Circus ANYmal: A Quadruped Learning Dexterous Manipulation with Its Limbs

Automatic Curriculum Design for Object Transportation Based on Deep Reinforcement Learning

Contact Info

Product

Resources

About