Autonomous robotics arm grasping object using reinforcement learning
2025-06-28 16:25:30 - Adil Khan
Autonomous robotics arm grasping object using reinforcement learning
Project Area of Specialization Artificial IntelligenceProject Summary
Since we know that the field of robotics has been rapidly developing in recent years. And related works train robotics agent with reinforcement learning has been a major focused on research. And our device is based on pick and place operation. Robotic arms have been used in industry for years to automate repetitive, strenuous, and complex tasks in which speed and precision are critical.
An engineer does not directly develop the code to apply the forward and inverse kinematics to complete each robotic task. To reduce difficulty with the implementation of robotics in factory settings, reinforcement learning (RL) is becoming a popular alternative to task-specific programming. The goal of RL in robotics is to enable the agent (the robotic arm) to complete basic operations without direct command programming, and to handle changes in the task without reprogramming. Example of a task which a robotic agent should be able to perform with RL.
The goal for completing a pick-and-place operation without task-specific programing is to allow the agent to independently perform actions in the environment, then learn the optimal strategy for its future tasks based on its experiences . The learning process is analogous to a child learning to walk. A child must first attempt the motions of moving limbs, rolling over, and crawling, before it can complete the target action of walking. The child’s behavior is driven by rewards from its environment after the completion of each task. Positive affirmation from the child’s parents is an example of an environmental reward which would drive the walking behavior
Autonomous robots is a challenge because they are intelligent machines, capable of performing tasks in the world by themselves, without explicit human control. It is challenging due to its power to taking decisions on its own. For that, we require an agent who is capable of continued observations and actions. We had faced many problems while implementing and interacting with Robert directly and in an unknown environment, so that is why we adopt the machine learning method because it’s a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
We specifically use Reinforcement learning in our project because it is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.
We have divided our work into two portions: First, to create an agent, and Second implement it into real-world applications like Universal Robots UR5, a highly flexible robotic arm that enables safe automation of repetitive, risky tasks. The UR5 flexible robot is ideal to optimize low-weight collaborative processes, such as picking, placing, and testing.
Aim:
? By implementing a reinforcement learning algorithm we are designing a model for robotic manipulation tasks like grasping the objects via difficult skills and learning.
Objectives:
? To design a model.
? To interact a model with any environment.
? To simulate a model for better understanding to perform the tasks.
? Plug a reinforcement method.
We use reinforcement learning to create the model and make it interact with real-world applications, that we divide our methodology into five steps which are
Step: 1
We know that can’t implement directly Reinforcement learning into robot.so we need to create an agent for based on our project.
Step: 2
OpenAI Gym is used to work out reinforcement learning algorithms. It helps us to create an agent. In this step, we create an agent that is placed in an environment and it will learn through its steps and observations, with these observations, it takes a decision and gains reward from the environment in the form of negative and positive points. Robotic arm moving around -50cm and reward is move arm -5cm to 0 around object.
Step: 3
In this step, we use Mujoco Physics simulator to check that the decisions that are being taken by the agent are right or wrong and check their effects on our environment or model. The benefit of training in a simulated environment is that the agent can perform thousands of training iterations in a short period, without any wear effects on the online device
Step: 4
We train our model by examining many examples and attempting to find a model that minimizes loss and chooses suitable decisions.
Step: 5
Now we check which reinforcement learning algorithm is suitable or best for accuracy and helpful in increasing the efficiency for that we use DDPG and SAC algorithm and we conclude that SAC is best for training because it takes less time in learning as compared to DDPG.
Step: 6
In this, we design a UR5 robotic arm and applies our model into device with help of ROS (it helps control the robot with the model via sending and receiving of information).
Self-motivated learning could be developed for the robotic agents, by rewarding “play”. The agent could be taught to extend their learning by completing a self-selected activity. In the end we have a device that we can use in future for manufacturing large scale devices that will be used by many users in different fields like engineers at civil sites etc.
Applications:
? Civil sites
? Palletizing
? Manufacturing
? Agriculture
? Pick and Place
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 23500 | |||
| Set Acrylic Plates | Equipment | 1 | 300 | 300 |
| Adeept Robotic Arm Driver Board(Compatible with Arduino) | Equipment | 1 | 5000 | 5000 |
| 0.96'' OLED Display | Equipment | 1 | 430 | 430 |
| Servo(MG90S) | Equipment | 6 | 300 | 1800 |
| Battery Holder | Equipment | 1 | 70 | 70 |
| Cross Socket Wrench | Equipment | 1 | 1000 | 1000 |
| Cross Screwdriver | Equipment | 1 | 600 | 600 |
| Winding Pipe | Equipment | 1 | 2000 | 2000 |
| Bearing | Equipment | 1 | 3000 | 3000 |
| Suction Cup | Equipment | 4 | 700 | 2800 |
| Micro USB Cable | Equipment | 1 | 500 | 500 |
| Other Necessary Accessories(Wires, Nuts, Screws, Copper Standoffs, etc | Equipment | 1 | 1000 | 1000 |
| Thesis | Miscellaneous | 5 | 1000 | 5000 |
| VM Used(Elastic Compute Cloud) | Miscellaneous | 0 | 0 | 0 |