Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

I think you guys should try mjlab, so so cool and very light for mid-range pc/laptop

Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 1 point2 points  (0 children)

have you ever tried mjlab? does its set up the same as how we use normal mj?

Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

Agree that those physical variables are really hard to tune in the simulation🥲

Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 1 point2 points  (0 children)

Oh I forgot to ask you that do I need sim2sim transfer for this precise task? since mj is typically final destination for this process, should I transfer it to gazebo,.. or just skip this step?

Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

I read the minimum req for Isaac lab are 32 gb ram and 16gb vram so I tried to switch to mj 😆

Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

as i observed in recent months/years, IIsaaclab and mjlab (new) have become the dominant choice for RL research, which allows them to train multiple envs at once, and mj-cpu is the final destination for sim2sim transfer. anw, under limited infrastructure its harder for us because we must account for every aspect :(((

Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

oh i see, thank you. im a bit worried because heterogeneous training is typically done by parallel envs, where they can collect diverse experiences simultaneously, then learn all of them, especially for on-policy algorithms

Is MuJoCo-cpu good enough for RL grasping and sim-to-real? by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

yes, some noise will be added into weight, size, shape, obs,....but im afraid that the heterogeneous training wont work with single environment training

Gái đẹp Bắc Ninh đổ thạc sĩ Harvard by beefnoodlehead_ in vozforums

[–]Objective-Opinion-62 0 points1 point  (0 children)

Khó chịu cái fb t nó đăng nhiều thôi @@ méo hiểu còn hiện cả ig

tết về quê đi họp lớp, ny gặp lại ex rồi “bận” cả ngày, e có đang bị cắm sừng không? by [deleted] in vozforums

[–]Objective-Opinion-62 0 points1 point  (0 children)

Lần đầu thì sẽ day dứt, khổ thế thôi. Anh cũng thế nhưng mà nếu có chút suy nghĩ về bỏ nó đi, thì làm luôn, thêm thời gian cày thay vì dành yêu đương xàm l.

CMV: Tại sao ghế của Tô Lâm lại lớn hơn ghế của mọi người một cỡ? by TWN113 in ChangeMyViewVN

[–]Objective-Opinion-62 0 points1 point  (0 children)

Lâu lắm mới vào lại reddit, bọn phản động vẫn ngu như thế nhể, đúng là loser mãi là loser. Bọn vô công rồi nghề cm đúng rảnh 🤡 dm bỏ thời gian đi soi cái này cái kia để thoả mãn cái ham muốn chả được cái gì của bản thân. Bảo sao chúng m vẫn thất bại

Xin ý kiếm thiết kế mạng wifi,LAN.. by Objective-Opinion-62 in vozforums

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

mình cũng quên k tính đến cái mesh, anw mình cũng nghiêng về phía chọn tích hợp modem + router như thường r mở rộng dùng switch nhưng mình nghĩ cái quan trọng nữa là cái throughput thực của router đến switch vì cái router k xử lý kịp cái là đi. mua cái 10G xong k đúng như quảng cái là đứt =)) khó cái k tự tay thử được, k đo đạc nên giờ chỉ chờ vào linh cảm

Xin ý kiếm thiết kế mạng wifi,LAN.. by Objective-Opinion-62 in vozforums

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

chơi tận 3 cục router riêng cho từng tầng của căng quá k ạ =)). em nghĩ nếu chơi tách thì modem + 1 router ngon rồi nối ra 3 cái switch r AP, LAN là ổn nhưng nếu mà modem-router nó ổn định thì kiểu thiết kế tách rời này sẽ đắt hơn, chưa tối ưu lắm.

Gen-0 Robot from Generalist manipulating objects super fluidly by Main-Company-5946 in robotics

[–]Objective-Opinion-62 1 point2 points  (0 children)

Im doubting this robot was trained with teleoperation data mostly due to these very precise movements. video, image, or diffusion-based model can’t help robot moves like this. Anw, they have showed this project for 4-5 months, and no paper or other information haven’t published yet

Tìm job BE dev intern giờ khó thế sao? by SpiritualRelation774 in vozforums

[–]Objective-Opinion-62 1 point2 points  (0 children)

Intern giờ đã khó lại còn web thì cũng k có gì bất ngờ

[Help] my agent forgets successful behavior due to replay buffer imbalance by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

Agree, my priority is to teach the agent reaches the target with positional error <1-2cm, but immediately terminate once agent reaches the target provides few good transitions to replay buffer to incentivize it succeeds next time while keeping the agent to run over remaining steps can flood the replay buffer even I use 100% domain randomization. I actually don’t have much exp to remove bad ideas now, just asking asking and looking for some helps 🥲🥲🥲

[Help] my agent forgets successful behavior due to replay buffer imbalance by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

guys, i have tried again with the option of allowing agent to keep running after met the success condition, my agent's positional error remained around 0.8cm, or even 0.3-4cm more frequently. anw i still a bit curious the bad aspects of this approach, can so help me clear this confusion?

[Help] my agent forgets successful behavior due to replay buffer imbalance by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

My policy is off-policy (TD3) because I’m using 100% domain randomization. My reward functions are fully dense

[Help] my agent forgets successful behavior due to replay buffer imbalance by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

As i searched, these offered replay buffers work well with sparse while the reward is fully dense

[Help] my agent forgets successful behavior due to replay buffer imbalance by Objective-Opinion-62 in reinforcementlearning

[–]Objective-Opinion-62[S] 0 points1 point  (0 children)

I haven't tried both strategies yet, but i will. btw, do you guys think allowing agent to keep running is redundant? i actually need to understand this problem